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1 | Major Reports by Organisations and Governments. | Abstract | Description/ Quick Summary | URL | Theme 1 - Overall impact of AI | Theme 2 - Impact on Professions | Theme 3 - Most affected and most secure groups | Theme 4 - AI drives people or people drive AI | Theme 5 - Ethics | Theme 6 - Regulating AI | Theme 7 - AI and related technologies | Theme 8 - Workforce, employment, skills & education | Theme 9 - Country Studies | Theme 10 - Diversity and other impacts | Theme A - AI and human dimension | Theme B - Customer/ stakeholder experience | Theme C - Specific AI technologies | Theme D - Ethics | Theme E - Future of the Profession | |||||||
2 | S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves (2017). Reshaping Business with Artificial Intelligence. MIT Sloan Management Review and The Boston Consulting Group | Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts. Building on data rather than conjecture, the research is based on a global survey of more than 3,000 executives, managers, and analysts across industries and in-depth interviews with more than 30 technology experts and executives. | https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/ | ✓ | ✓ | |||||||||||||||||||||
3 | Artificial Intelligence and Robotics and their Impact on the Workplace (2017). International Bar Association Global Employment Institute | https://www.ibanet.org/Article/NewDetail.aspx?ArticleUid=012a3473-007f-4519-827c-7da56d7e3509 | ✓ | ✓ | ✓ | |||||||||||||||||||||
4 | Dame Wendy Hall and Jérôme Pesenti (2017). Growing Artificial Intelligence in the UK. Department for Digital, Culture, Media & Sport and Department for Business, Energy & Industrial Strategy | https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk | ✓ | ✓ | ✓ | ✓ | ||||||||||||||||||||
5 | The impact of artificial intelligence on work: An evidence synthesis on implications for individuals, communities, and societies (2018). British Academy and the Royal Society | An evidence-based synthesis on the implications of AI for individuals, communities, and societies. Good overview, comprehensive referencing, good definitions provided.It is contended that 10–30% of jobs in the UK are highly automatable. Many new jobs will also be created in AI linked roles. Changes to work will tend to affect lower-paid and lower-qualified workers more than others. This suggests there are likely to be transitional effects that cause disruption for some people or places. One of the greatest challenges raised by AI is therefore a potential widening of inequality, at least in the short term, if lower-income workers are disproportionately affected and benefits are not widely distributed. | https://www.thebritishacademy.ac.uk/sites/default/files/AI-and-work-evidence-synthesis.pdf | ✓ | ✓ | ✓ | ||||||||||||||||||||
6 | The Impact of Artificial Intelligence On Work (2018). An evidence review prepared for the Royal Society and the British Academy | The review looks at current impact of AI on work and thoughts for the future. Presently, technological change is a key driver of economic growth. However, the invention, diffusion and effective use of new technology are in turn likely to be influenced by other factors, including economic conditions, institutions, and social conditions. Recent automation does not seem to have led to overall decline in employment levels, but there have been income losses for low-educated workers employed in the manufacturing sector. Employment losses in manufacturing have typically been compensated by increasing employment in services, leading to stable or growing overall employment levels. Future: In the long term, the initial decline in work will be compensated, however, workers who have been directly displaced could experience a fall in their earnings relative to other workers(and lead to increased inequality if the displaced group is mostly composed of low earners. There is some concern that digital technology may enable large firms to increase and maintain their market power. Studies in this literature find that between 10% and 30% of jobs in the United Kingdom could be automated at some point in the coming decades. However, a large majority of jobs involve performing at least some tasks that have been assessed as not suitable for automation. | https://royalsociety.org/-/media/policy/projects/ai-and-work/frontier-review-the-impact-of-AI-on-work.pdf | ✓ | ✓ | ✓ | ||||||||||||||||||||
7 | Artificial Intelligence: Australia’s Ethics Framework. A Discussion Paper (2019). Australian Government, Department of Industry, Innovation & Science | A major Australian Government Report on ethics in AI. Artificial Intelligence (AI) has the potential to increase well-being; lift the economy; improve society by, for instance, making it more inclusive; and help the environment by using the planet's resources more sustainably. For Australia to realise these benefits however, it will be important for citizens to have trust in the AI applications developed by businesses, governments and academia. One way to achieve this is to align the design and application of AI with ethical and inclusive values. A range of topics discused including transparency, accountability, data governance, behavioural predictions etc. | https://consult.industry.gov.au/strategic-policy/artificial-intelligence-ethics-framework/supporting_documents/ArtificialIntelligenceethicsframeworkdiscussionpaper.pdf | ✓ | ✓ | ✓ | ✓ | |||||||||||||||||||
8 | The Future of Work (2019). Institute for Public Relations | This Institute for Public Relations (IPR) report investigates the future of work and the impact of several factors on the changing nature of work, including a shifting technological landscape, the growth of globalization, and the juxtaposition of new and tenured members of the workforce. The intent of this project was to investigate the three interlinked dimensions of an organization: the work (the what), the workforce (the who), and the workplace (the where).Each offers unique challenges and opportunities for organizational (internal) communication. | https://instituteforpr.org/wp-content/uploads/The-Future-of-Work-V5.pdf | ✓ | ✓ | ✓ | ||||||||||||||||||||
9 | Artificial intelligence: opportunities and implications for the future of decision making (2016). Government Office for Science | AI offers huge potential to enable more efficient and effective business and government, but the use of artificial intelligence brings with it important questions about governance, accountability and ethics. Realising the full potential of artificial intelligence and avoiding possible adverse consequences requires societies to find satisfactory answers to these questions. This report sets out some possible approaches, and describes some of the ways government is already engaging with these issues. It recognises that Government is a special body, with unique obligations that do not fall on private organisations. It must be transparent about the way it acts, follow due process, and be accountable to its citizens. Impacts on labour market and ethical implications of AI discussed in detail. Good definitions of terms such as machine learning. Well referenced. | https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/566075/gs-16-19-artificial-intelligence-ai-report.pdf | ✓ | ✓ | |||||||||||||||||||||
10 | AI in the UK: ready, willing and able? (Report of Session 2017-19). House of Lords Select Committee on AI | Comprehensive report on AI as a whole. Areas covered: definitions of AI, related terms and categories of AI; developemnt of AI; recent reports on AI; impact on politics; public understanding of AI and engagement with it; access and control of data; making AI intelligible (transparency, explainability); bias and prejudice; data monopolies; investemnt in AI; academic research and access to skilled researchers; threats and opportunities for employment; impact on social and political cohesion; AI and healthcare; mitigating the risks of AI (legal issues, misuse, autonomous weapons); the future for AI - support for development, Government Office for AI, regulation, AI code for pethics and protection. | https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf | ✓ | ✓ | ✓ | ||||||||||||||||||||
11 | Artificial Intelligence, Automation, and the Economy. Executive Office of the President (2016). White House | Following up on the Administration’s previous report, Preparing for the Future of Artificial Intelligence, which was published in October 2016, this report further investigates the effects of AI-driven automation on the U.S. job market and economy, and outlines recommended policy responses. Concludes biggest effect on lower-skilled and lower paid workers with up to 47% of US jobs being at risk (high estimate) of automation of some kind. Jobs less affect the future include those requiring manualdexterity, creativity, social interaction and 'intelligence' . | https://obamawhitehouse.archives.gov/sites/whitehouse.gov/files/documents/Artificial-Intelligence-Automation-Economy.PDF | ✓ | ✓ | ✓ | ✓ | |||||||||||||||||||
12 | Ethics guidelines for trustworthy AI (2019). European Commission | High-Level Expert Group on AI ethics guidelines for trustworthy artificial intelligence. This follows the publication of the guidelines' first draft in December 2018. Basisc principles are that AI should be lawful, ethical and robust. Topics covered include: human agency, privacy and data governance, transparency, diversity, societal and environmental well being and accountability. | https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai | ✓ | ✓ | |||||||||||||||||||||
13 | AI Forum NZ Discussion Paper: The Potential Economic Impacts of AI Literature Review (2018). AI Forum New Zealand | This Artificial Intelligence Forum of New Zealand (AIFNZ) discussion paper provides a detailed literature review of recent global research into the potential economic impacts of artificial intelligence (AI). It was conducted to provide insights for the preparation of the AI Forum’s 2018 research report, Artificial Intelligence – Shaping a Future New Zealand. It askes 4 questions: What will be the size of the overall economic impacts? Will this really happen any time soon? Will we notice? This literature review also examines the potential economic and labour market effects of the adoption of AI concluding that the greatest productivity gains are in capital intensive sectors like manufacturing and transport and that low skilled workers are most at risk from AI because the potential to automate their jobs is higher than for the highly qualified. technical limitations of automation include perception and manipulation tasks in unstructured environments, creative and social intelligence. Automation will also produce new jobs for those who create, program, maintain and support AI. | https://aiforum.org.nz/wp-content/uploads/2018/07/AIForum-DiscussionPaper-EconomicimpactofAILiteratureReview-May2018.pdf | ✓ | ✓ | ✓ | ||||||||||||||||||||
14 | Books | Abstract | Description/ Quick Summary | URL | Theme 1 - Overall impact of AI | Theme 2 - Impact on Professions | Theme 3 - Most affected and most secure groups | Theme 4 - AI drives people or people drive AI | Theme 5 - Ethics | Theme 6 - Regulating AI | Theme 7 - AI and related technologies | Theme 8 - Workforce, employment, skills & education | Theme 9 - Country Studies | Theme 10 - Diversity and other impacts | Theme A - AI and human dimension | Theme B - Customer/ stakeholder experience | Theme C - Specific AI technologies | Theme D - Ethics | Theme E - Future of the Profession | |||||||
15 | Boddington, P. (2017). Towards a code of ethics for artificial intelligence (2017). Cham: Springer International Publishing. | The book investigates how to produce realistic and workable ethical codes or regulations in the rapidly developing AI field to address the immediate and realistic longer-term issues facing us. Boddington spells out the key ethical debates concisely, exposing all sides of the arguments, and addresses how codes of ethics or other regulations might feasibly be developed, looking for pitfalls and opportunities, drawing on lessons learned in other fields, and explaining key points of professional ethics. The book provides a useful resource for those aiming to address the ethical challenges of AI research in meaningful and practical ways. | DOI: 10.1007/978-3-319-60648-4 | ✓ | ||||||||||||||||||||||
16 | Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford, UK: Oxford University Press. | An analytical and philosophical overview of a possible path to the development of a superintelligence, and how it might affect humanity and its future. Bostrom poses difficult questions, analyses various approaches and risks, and provides strategic recommendations on how we might proceed. | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||||||
17 | Daugherty, P., & Wilson, H. (2018). Human + machine: Reimagining work in the age of AI. Boston, MA: Harvard Business Review Press. | A discussion of the way artificial intelligence is disrupting businesses processes. The authors believe workplaces will evolve into a series of human AI agent collaborations. They propose models for various new roles within organizations and discuss how people might interact with machines. | ✓ | ✓ | ✓ | |||||||||||||||||||||
18 | Harari, Y.N. (2015). Homo deus: A brief history of tomorrow. Toronto, ON: Signal. | Harari uses data to support his thesis that humanity has overcome three of its biggest existential challenges, ʺfamine, plague, and warʺ, and is now on a quest for immortality. He offers a historical overview of how people came to dominate the planet and speculates that our desire for longer lives could lead to the development of a conscious artificial intelligence and the possible end of the human journey. | ✓ | |||||||||||||||||||||||
19 | Harari, Y.N. (2018). 21 lessons for the 21st century. Toronto, ON: Signal. | Drawing on some of his earlier work, Harari discusses 21 issues and how they might affect and/or transform humanity. Topics include politics, religion, secularism, culture, science, data, and artificial intelligence. | ✓ | ✓ | ||||||||||||||||||||||
20 | Loukides, M., Mason, H., & Patil, D.J. (2018). Ethics and Data Science. Sebastopol, CA: O’Reilly Media. | Three data scientists examine the ethics and risks surrounding the use of personal data, privacy, and disclosure in technology and AI. They propose a five-step process that could help researchers build ethical considerations into their projects. | https://learning.oreilly.com/library/view/ethics-and-data/9781492043898/ch01.html | ✓ | ||||||||||||||||||||||
21 | Penn, C.S. (2017). AI for marketers: An introduction and primer. | Penn uses plain language to explain what big data, predictive analytics, and artificial intelligence are and how they might change the marketing landscape. He offers examples of how marketers can incorporate AI, data, and machine learning to achieve business goals. | https://gumroad.com/l/aiformarketing | ✓ | ✓ (marketing) | |||||||||||||||||||||
22 | Pentland, A. (2014). Social physics: How social networks can make us smarter. New York, NY: Penguin. | Pentland uses data to predict human behaviour, and understand the flow of ideas, and the social patterns underlying how people come together to collaborate and solve problems. | ✓ | ✓ | ||||||||||||||||||||||
23 | Sullivan, S., and Zutavern, A. (2017). The mathematical corporation: Where machine intelligence and human ingenuity achieve the impossible. New York, NY: PublicAffairs. | A business case encouraging C-suite leaders to learn how their organizations can incorporate data science, predictive analytics, and artificial intelligence across the enterprise. Includes several examples and case studies. | ✓ | ✓ (business case for AI) | ||||||||||||||||||||||
24 | Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. New York, NY: Oxford University Press. | A seminal book on the impact of technology on the future of the professions. | BOOK - available online as an ebook | ✓ | ||||||||||||||||||||||
25 | Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. New York, NY: Alfred A. Knopf. | Tegmark explores the evolution of intelligence and how a future step in the process may be the development of a conscious AI. He presents several risk scenarios and poses ethical questions around how a sentient machine might alter or destroy human existence. He encourages the consideration of long-term consequences in order to plan for a future with beneficial AI. | ✓ | ✓ | ✓ (future of AI, Intelligent machines) | |||||||||||||||||||||
26 | Webb, A. (2019). The big nine: How the tech titans and thinking machines could warp humanity. New York, NY: PublicAffairs | Webb looks at how the development and commercialisation of artificial intelligence is currently controlled by nine private organizations, the G-MAFIA in the U.S. (Google, Microsoft, Amazon, Facebook, IBM, and Apple), and BAT in China (Baidu, Alibaba, and Tencent). She argues that unless there is some oversight and policy, developments will be made for the good of the corporations, rather than the good of humanity. | ✓ | ✓ (dominance of big tech companies in AI) | ||||||||||||||||||||||
27 | Academic Articles | Abstract | Description/ Quick Summary | URL | Theme 1 - Overall impact of AI | Theme 2 - Impact on Professions | Theme 3 - Most affected and most secure groups | Theme 4 - AI drives people or people drive AI | Theme 5 - Ethics | Theme 6 - Regulating AI | Theme 7 - AI and related technologies | Theme 8 - Workforce, employment, skills & education | Theme 9 - Country Studies | Theme 10 - Diversity and other impacts | Theme A - AI and human dimension | Theme B - Customer/ stakeholder experience | Theme C - Specific AI technologies | Theme D - Ethics | Theme E - Future of the Profession | |||||||
28 | Singh, J., Flaherty, K., Sohi, R. S., Deeter-Schmelz, D., Habel, J., Le Meunier-FitzHugh, K., Malshe, A., Mullins. R, Onyemah, V. (2019). Sales profession and professionals in the age of digitization and artificial intelligence technologies: Concepts, priorities, and questions. Journal of Personal Selling & Sales Management, 39(1), 2-22. | Recognizing the rapid advances in sales digitization and artificial intelligence technologies, we develop concepts, priorities, and questions to help guide future research and practice in the field of personal selling and sales management. Our analysis reveals that the influence of sales digitalization technologies, which include digitization and artificial intelligence, is likely to be more significant and more far reaching than previous sales technologies. To organize our analysis of this influence, we discuss the opportunities and threats that sales digitalization technologies pose for (a) the sales profession in terms of its contribution to creating value for customers, organizations, and society and (b) sales professionals, in terms of both employees in organizations and individuals as self, seeking growth, fulfillment, and status in the functions they serve and roles they live. We summarize our discussion by detailing specific research priorities and questions that warrant further study and development by researchers and practitioners alike. | Article talks about the impact of AI on the sales profession and pofessionals in the sales industry and how AI impacts the quality and delivery of value for customers, society and organisations. | https://doi.org/10.1080/08853134.2018.1557525 | ✓ | ✓ | ||||||||||||||||||||
29 | Connell, W. J. (2018). Artificial intelligence in the legal profession-what you might want to know. Computer and Internet Lawyer, 35(9), 32-36. | During a recent webinar sponsored by LexisNexis, entitled “Artificial Intelligence & the Legal Profession,” Dennis Garcia, Assistant General Counsel for Microsoft Corporation, suggested that legal professionals should not fear artificial intelligence.1 According to Garcia, artificial intelligence is not a foe to attorneys, but a tool for all lawyers. He cites several sources to support his argument, including a recent New York Times article by Steve Lohr (somewhat cryptically) entitled “A. I. is Doing Legal Work. But it Won’t Replace Lawyers,Yet.” 2 Garcia’s assertions that artificial intelligence is an asset to lawyers and will not supplant or replace attorneys seems both convincing and sound—if you are legal counsel at Microsoft Corporation. But if you are not, then perhaps we as members of the legal profession need to really assess how this new technology will impact and change the practice of law, and what the role of the attorney will be. | Article discusses the issues and fears AI brings to the legal profession, such as AI replacing the need for lawyers and argues that the extent to which lawyers might be influenced is dependent on context. | n.a. | ✓ | ✓ | ||||||||||||||||||||
30 | Hoffmeister, J. (2018). Getting AI ready: How advances in artificial intelligence can benefit the legal profession. New Law Journal, 168(7793), 20. | n.a. | The article talks about how AI can revolutionise and impact the legal industry. Suggests the need for undertsanding the role of AI in providing high quality legal service. | https://www.newlawjournal.co.uk/content/getting-ai-ready | ✓ | |||||||||||||||||||||
31 | Sobowale, J. (2016). How artificial intelligence is transforming the legal profession. ABA Journal, 102(4), 46. | n.a. | Article highlights the transformation that legal profession has gone through over the past 20 years due to the rise of AI and how this has created a new generation of lawyers. | http://www.abajournal.com/magazine/article/how_artificial_intelligence_is_transforming_the_legal_profession | ✓ | |||||||||||||||||||||
32 | Bolinger, G. (2017). Artificial intelligence and the future of the accounting profession. CPA Practice Advisor, 27(8), 17. | The relentless advance of technology. You are aware of it. Whether you are boomer, a millennial, or even Gen Z (some call them the “iGeneration”). We can’t deny it. We live in a time of technological marvels. And the velocity of development related to technology is only accelerating. … So, what is the future of AI? What does it possibly mean for the CPA profession? Most simply it means that the profession will evolve. The role of the CPA will change. While there are many skeptics, I don’t buy it. Historically, advances in technology have changed jobs. To be fair, some jobs have simply vanished (there aren’t a lot of blacksmiths these days). | Discussion of the changes AI has brought and is about to bring to the accounting profession and possibly make parts of it obsolete. | n.a. | ✓ | ✓ | ||||||||||||||||||||
33 | Meskovic, E., Garrison, M., Ghezal, S., & Chen, Y. (2018). Artificial Intelligence: Trends in business and implications for the accounting profession. Internal Auditing, 33(3), 5-11. | We may only begin to hypothesize about artificial intelligence’s (AI’s) prospective implications, what it may mean for accountants, and its future role in financial services, audit quality, and predictive capabilities. Nonetheless, it has been made evident that the accounting profession does not intend to remain complacent as it strives to achieve a higher quality of service, and as a result, a higher degree of trust among the capital markets. | Article about the impact of AI on accounting and how this might strengthen the outcomes and bring about more trust and higher service quality among the capital markets. | n.a. | ✓ | |||||||||||||||||||||
34 | Fiksel, J. (1987). The impact of artificial intelligence on the risk analysis profession. Risk Analysis, 7(3), 277-280. | The term “artificial intelligence” (AI) often conjures up images of industrial robots and language translation machines. However, one of the fastest growing application areas for AI is in management decision-making support for both business and government. In particular, AI promises to have a significant impact on professionals working in the field of health, safety, and environmental risk management. AI scientists have created a new class of computer software, called “knowledge systems,” which can simulate human reasoning. Knowledge systems are usually defined as computer programs that give advice, solve problems, or perform other “intelligent” tasks. More generally, knowledge systems automate the application of knowledge, just as calculators automate the application of arithmetic or database management programs automate the indexing and retrieval of records. Thus, knowledge systems technology involves the computer-aided representation of human knowledge and reasoning in symbolic form. The design and development of knowledge systems requires a specific type of expertise called “knowledge engineering. The potential uses of knowledge systems are only beginning to be explored. Early applications tended to view the knowledge system as an independent agent that offered expertise in response to user requests. In fact, knowledge systems can form the basis of a much broader class of support tools that extend the scope and power of human knowledge processing. In the near future, risk analysts and risk managers wil be assisted in their day-to-day activities by powerful workstations that simplify much of the effort involved in searching for information, running analytic models, and interpreting the results. | Article explores the impact of AI and developments in AI on risk analysts and risk managers and praises its role in simplifying the complexity of decision making and the potential of AI for the future formation of those professions. | https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1539-6924.1987.tb00460.x | ✓ | ✓ | ||||||||||||||||||||
35 | O'Donnell, J. S. (1990). Artificial intelligence use in the legal profession: What are its liabilities? Software Law Journal, 4(1), 77. | The use of Artificially Intelligent systems, or what are commonly termed "expert systems," has dramatically increased in the legal field over the past five years.The utilization of expert systems has presented, and will continue to generate, unique and complex liability issues stemming from personal injuries which result from the use of either defective programs or incompetent attorney-users. This article will look at the potential liabilities artifically intelligent systems can bring to its users as its benefits are realized. | Article highlights the liabilities of AI for legal profession because, as O'Donnell argues, the benefits are largely realised, unlike its liabilities. | n.a. | ✓ | |||||||||||||||||||||
36 | Alarie, B., Niblett, A., & Yoon, A. H. (2018). How artificial intelligence will affect the practice of law. University of Toronto Law Journal, 68(1), 106-124. | Artificial intelligence is exerting an influence on all professions and industries. We have autonomous vehicles, instantaneous translation among the world’s leading languages, and search engines that rapidly locate information anywhere on the web in a way that is tailored to a user’s interests and past search history. Law is not immune from disruption by new technology. Software tools are beginning to affect various aspects of lawyers’ work, including those tasks that historically relied upon expert human judgment, such as predicting court outcomes. These new software tools present new challenges and new opportunities. In the short run, we can expect greater legal transparency, more efficient dispute resolution, improved access to justice, and new challenges to the traditional organization of private law firms delivering legal services on a billable hour basis through a leveraged partner-associate model. With new technology, lawyers will be empowered to work more efficiently, deepen and broaden their areas of expertise, and provide more value to clients. These developments will predictably transform both how lawyers do legal work and resolve disputes on behalf of their clients. In the longer term, it is difficult to predict what the impact of artificially intelligent tools will be, as lawyers incorporate them into their practice and expand their range of services on behalf of clients. | Article points out the following professions are being transformed, and some made obsolete: those related to autonomous vehicles, translation AI; otherwise the discussion rotates around both advantages and challenges that AI brings to the legal profession. | https://www.utpjournals.press/doi/abs/10.3138/utlj.2017-0052 | ✓ | ✓ | ||||||||||||||||||||
37 | Williams, P. (2019). Does competency-based education with blockchain signal a new mission for universities? Journal of Higher Education Policy and Management, 41(1), 104-117. | New technologies and the knowledge economy are destabilising graduate professions, with artificial intelligence and the analysis of ‘big data’ making significant impacts on formerly secure jobs. Blockchain technology, offering automated secure credentialling of undergraduate students’ activities and achievements, may significantly erode existing systems of assessment. The challenge for universities will be not only to maintain the relevance of their curricula but also to manage erosion of their current near-monopoly in awarding degrees. This paper envisions a landscape in which universities must outsource parts of their course delivery and assessment in order to remain competitive. It examines a potentially sustainable mission strategy: to move away from narrow academic disciplines towards an authentic learning curriculum focusing on the development of students as whole persons with rounded educations. This paper examines implications for the academy of the convergence of artificial intelligence, data analytics and blockchain technology. | Article examines implications of AI, data analytics and blockchain technology for universities, highlighting that they need to adapt to the changing landscape related to graduate professions. | https://www.tandfonline.com/doi/abs/10.1080/1360080X.2018.1520491 | ✓ | |||||||||||||||||||||
38 | McKamey, M. (2017). Legal technology: Artificial intelligence and the future of law practice. Appeal, Review of Current Law and Law Reform, 22, 45-57. | Lawyers are increasingly told that advanced technology is coming soon to their doorsteps and will radically change the nature of their work. Such premonitions are often vague and not particularly threatening to a profession that has happily operated in much the same way for over a century. This paper examines the notion that technology will radically disrupt the legal profession by first describing the drivers of modern technological progress and the recent rise of artificial intelligence (AI). It then considers what current technology trends might mean for the legal profession, concluding that technology is likely, in a relatively short period of time, to transform how legal services are delivered. | Article examines the theory that technology will radically change the legal profession and concludes that its prophecy is soon likely to be fulfilled. | https://journals.uvic.ca/index.php/appeal/article/view/16750 | ✓ | |||||||||||||||||||||
39 | Jackson, B. (2017). Artificial intelligence and the future of the accountancy profession. Accountancy Ireland, 49(1), 33. | not available for general download. | n.a. | n.a. | ✓ | |||||||||||||||||||||
40 | Bigda, J. (2018). The legal profession: From humans to robots. The Journal of High Technology Law, 18(2), 396. | Recently, there has been increased research and writing on the topic of robots or artificially intelligent programs having the capability of practicing law and replacing human lawyers.' To some extent, this is true in that artificially intelligent programs are being used to perform tasks traditionally performed by humans in the legal field, especially since technology is advancing, and clients are looking for more lean and cost efficient legal services. Major law firms across the country are consistently replacing associates with artificially intelligent programs that can do associate-level work more efficiently and accurately in the areas of document review and legal writing. Although nerve-racking for young associates and graduating law students, this seems like a good business strategy on the part of law firms because firms would no longer have to pay for paralegals and associates when firms could have artificially intelligent lawyers perform these tasks with a lesser risk of error for a lower cost. | Article highlights how AI replaces paralegals, i.e. fresh graduates working as associates at law firms due to AI being more efficient and less prone to error which is beneficial to the law firms but not good news for law school graduates. | https://cpb-us-e1.wpmucdn.com/sites.suffolk.edu/dist/5/1153/files/2018/05/THE-LEGAL-PROFESSION-FROM-HUMANS-TO-ROBOTS-121x1wu.pdf | ✓ | |||||||||||||||||||||
41 | Yoon, A. H. (2016). The post-modern lawyer: Technology and the democratization of legal representation. University of Toronto Law Journal, 66(4), 456-471. | In recent years, scholars and the media have chronicled the challenges facing the legal profession: notably declining law school enrolment, higher unemployment for law graduates, and technological advances that increasingly threaten the livelihood of lawyers. To many, these developments confirm suspicions that the legal profession is in an irreversible decline. This article takes a more sanguine view about the profession’s future. While technology has indeed contributed to the current struggles of the legal labour market, it has centred thus far on automating the rule-based tasks of lawyers. The latest technologies – still in their nascent stages – focus on facilitating how lawyers analyze more complex legal questions. I argue that these intelligence augmentation technologies will reduce the cost of legal services for both lawyers and litigants, democratizing the legal profession in the process. | Article highlights how AI has contributed to the crisis in the legal labour market (lower enrollment at universities, higher unemployment of law graduates and technological advances replacing the need for legal employees), but also highlights that these might, in the process, democratise the legal profession. | https://www.utpjournals.press/doi/full/10.3138/UTLJ.4007 | ✓ | ✓ | ✓ | |||||||||||||||||||
42 | Jones, C. P. A. (2018). AI and the legal profession: Could artificial stupidity and responsibility avoidance prove to be the biggest agents of change? South Carolina Law Review, 69(3), 637. | Article highlights that AI has already replaced certain jobs related to legal practice and might continue to do so if not stopped. | n.a. | ✓ | ||||||||||||||||||||||
43 | De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2017). Human resources for big data professions: A systematic classification of job roles and required skill sets. Information Processing and Management, 54(5), 807-817. | The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace. | Article analyses the newly emerged professions related to Big Data Analytics and highilghts how these need to be considered by businesses, HR practitioners and educators. | https://doi.org/10.1016/j.ipm.2017.05.004 | ✓ | |||||||||||||||||||||
44 | Flood, J., & Robb, L. (2019). Professions and expertise: How machine learning and blockchain are redesigning the landscape of professional knowledge and organization. University of Miami Law Review, 73(2), 443. | Machine learning has entered the world of the professions with differential impacts. Automation will have huge impacts on the nature of work and society. Engineering, architecture, and medicine are early and enthusiastic adopters of automation. Other professions, especially law, are late and, in some cases, reluctant adopters.' This Article examines the effects of artificial intelligence ("AI') and Blockchain on professions and their knowledge bases. We start by examining the nature of expertise in general and the function of expertise in law. Using examples from law, such as Gulati and Scott's analysis of how lawyers create (or don't create) legal agreements, we show that even non-routine and complex legal work is potentially susceptible to automation. However, professions are different from other occupational groups because they include both indeterminate and technical elements that make pure automation difficult to achieve. We consider the future prospects of AI and Blockchain on professions and hypothesize that as the technologies mature, they will incorporate more human work through neural networks and Blockchain applications, such as the distributed autonomous organization ("DAO"). We argue that in the law and the legal profession, the role of lawyer as trusted advisor will again emerge as the central point of value. | Article highlights that automation changes within the landscape of the legal profession and despite making some of the jobs obsolete, it will not replace the profession and, in time, lawyers will regain the place that has been partially substituted by AI. The authors also highlight that whilst blue collar jobs might vanish in light of automation, this is not likely to happen in white collar professions which rely on human judgement and solving complex issues. | https://repository.law.miami.edu/umlr/vol73/iss2/6 | ✓ | |||||||||||||||||||||
45 | Galloway, C., & Swiatek, L. (2018). Public relations and artificial intelligence: It’s not (just) about robots. Public Relations Review, 44(5), 734-740. | Organizations of all kinds, as well as their in-house or agency public relations teams, increasingly co-opt Artificial Intelligence (AI) to enhance their capabilities. This paper examines a relatively new topic that has received little scholarly attention: the growing relationship between AI and public relations. It outlines several key roles that AI may play in future, based on trends in other industries, and considers the implications for public relations practitioners, their clients and employers. It therefore launches a dialogue about the diversity and extent of AI’s uses in public relations practice. The paper argues that, to date, commentators have placed too much emphasis on AI’s potential for task automation; AI’s broader technological, economic and societal implications for public relations warrant greater critical attention. This does not imply that practitioners need become expert technologists; rather, they should develop a sufficient understanding of AI’s present and potential uses to be able to offer informed counsel. | Article discusses the role of AI in the PR profession and recommends that PR practitioners should understand how AI can be used to enhance the counselling capability of PR practitioners. | http://doi.org/10.1016/j.pubrev.2018.10.008 | ✓ | |||||||||||||||||||||
46 | Wilson, H. J., Daugherty, P. R., & Morini-Bianzino, N. (2017). The jobs that artificial intelligence will create. MIT Sloan Management Review, 58(4), 14-16. | The threat that automation will eliminate a broad swath of jobs across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur. It can be a distressing picture. But here’s what we’ve been overlooking: Many new jobs will also be created — jobs that look nothing like those that exist today. In Accenture PLC’s global study of more than 1,000 large companies already using or testing AI and machine-learning systems, we identified the emergence of entire categories of new, uniquely human jobs. These roles are not replacing old ones. They are novel, requiring skills and training that have no precedents. (Accenture’s study, “How Companies are Reimagining Business Processes with IT,” will be published this summer.) More specifically, our research reveals three new categories of AI-driven business and technology jobs. We label them trainers, explainers, and sustainers. Humans in these roles will complement the tasks performed by cognitive technology, ensuring that the work of machines is both effective and responsible — that it is fair, transparent, and auditable. | Article states that the fear of AI replacing jobs, thus putting people out of employment, do not take into consideration the new jobs that AI will create which will employ humans as complements to emerging AI processes. | http://ilp.mit.edu/media/news_articles/smr/2017/58416.pdf | ✓ | |||||||||||||||||||||
47 | Singh, S. (2018). Will artificial intelligence replace PR professionals? Gulf Marketing Review. | Opinion piece on robots in the PR profession arguing that the AI will not replace PR practitioners - at least for now. | n.a. | ✓ | ||||||||||||||||||||||
48 | Degani, A., Goldman, C. V., Deutsch, O., & Tsimhoni, O. (2017). On human–machine relations. Cognition, Technology & Work, 19(2), 211-231. | This paper presents an approach to human–machine interactions based on the concept of teamwork and the psychological theory of object relations. We envision the human and the machine in a close relationship that has many aspects of human-to-human relations. Not only does the machine have to relate to and accommodate human wants and needs, but also, to some extent, the human is called to reciprocate. We propose a framework consisting of eleven attributes that describe generic processes in teamwork: commitment, goal definition, common ground, belief, planning, transparency, sensitivity, caring, responsibility, trust, and reflection. Using an automotive climate control system as an example, we show how some of these attributes can be used to evaluate user interactions and point to new design opportunities. Based on results from a pilot study of driver interaction with the climate control system, we operationalized sensitivity and caring for other team members, encapsulated them in a computational architecture, and implemented a control interface. The evaluation of the control interface during a driving experiment suggests that it is markedly better than a regular interface and is almost as good as a human expert who interacts with the climate control system in response to the driver’s needs and wants. | Article explores the human-robot interaction in the context of a climate control and finds that the AI is almost as good as a human expert who interacts with a climate control system. | doi:10.1007/s10111-017-0417-3 | ✓ | |||||||||||||||||||||
49 | Papsdorf, C. (2015). How the internet automates communication. Information, Communication & Society, 18(9), 991-1005. | This article looks at how Internet media bring about the automation of social communication. Whereas communication originally took place between humans, human–machine interaction is now replacing this traditional type of communication in a growing number of areas of society. This form of mediatized communication is characterized by a high degree of rationality (amongst other things), in as much as the applications are consequently governed by criteria of efficiency. As a result, important areas of social communication are becoming detached from subjective factors and are governed by a goal-oriented merit principle. The article identifies eight areas of automated communication, discusses five different types of rationalization (increase in efficiency, accumulation, recombination, correlation, and the adaptation of system logics from other systems), and looks at the consequences for society of this new type of Internet communication. | Article discusses the emerging computer-human communication and highlights that such communication is highly rational and goal-driven, eliminating the aspect of subjectivity; whilst it does not directly consider any profession, there are, logically, impacts on professions related to social communication. | https://doi.org/10.1080/1369118X.2015.1008539 | ✓ | |||||||||||||||||||||
50 | Green, E., & Adam, A. (1998). On-line leisure: Gender, and ICTs in the home. Information, Communication & Society, 1(3), 291-312. | Research into office automation originally acted as a catalyst for research into gender perspectives on information technology. Whilst a fuller picture of women's use of ICTs is emerging, there has been little research on women's leisure use of ICTs, particularly within a domestic setting. Added to the way in which the leisure studies discipline has discovered gender as a variable, this is somewhat surprising. In this paper we argue that current debates on 'virtual culture' would be enriched by analysing the gender dimensions of the use of ICTs for leisure. In addressing personal agency we see women as active agents rather than passive victims of existing structures. The paper addresses negotiations around leisure and the use of technology in the home and how this illuminates the construction of gender identities. The ways in which work and leisure seep into one another are examined through a consideration of electronic mail and the World Wide Web. Although we conclude that women's leisure access is played out within familiar constraints of time and space there are glimpses of a more promising future to be found. | Not direct impact of AI on professions, but to office automation. There are likely to be gender implications and particularly on occupations that have had significant numbers of women in the workforce, for exampe, those linked to leisure (hospitality, tourism and similar). The paper also looks at ICT and women's leisure activities. | https://doi.org/10.1080/13691189809358971 | ✓ | ✓ | ||||||||||||||||||||
51 | Shank, D. B., DeSanti, A., & Maninger, T. (2019). When are artificial intelligence versus human agents faulted for wrongdoing? moral attributions after individual and joint decisions. Information, Communication & Society, 22(5), 648-663. | Artificial intelligence (AI) agents make decisions that affect individuals and society which can produce outcomes traditionally considered moral violations if performed by humans. Do people attribute the same moral permissibility and fault to AIs and humans when each produces the same moral violation outcome? Additionally, how do people attribute morality when the AI and human are jointly making the decision which produces that violation? We investigate these questions with an experiment that manipulates written descriptions of four real-world scenarios where, originally, a violation outcome was produced by an AI. Our decision-making structures include individual decision-making – either AIs or humans – and joint decision-making – either humans monitoring AIs or AIs recommending to humans. We find that the decision-making structure has little effect on morally faulting AIs, but that humans who monitor AIs are faulted less than solo humans and humans receiving recommendations. Furthermore, people attribute more permission and less fault to AIs compared to humans for the violation in both joint decision-making structures. The blame for joint AI-human wrongdoing suggests the potential for strategic scapegoating of AIs for human moral failings and the need for future research on AI-human teams. | Article focuses on moral judgement in decision-making and attributing responsibility for the decisions in AI, humans and AI-human interactions. | https://doi.org/10.1080/1369118X.2019.1568515 | ✓ | |||||||||||||||||||||
52 | McClain, N. (2018). The horizons of technological control: Automated surveillance in the New York subway. Information, Communication & Society, 21(1), 46-62. | Surveillance technologies may be capable of monitoring a domain, but they need a sufficiently orderly domain to monitor. This article examines the secretive effort to institute artificial-intelligence based ‘smart surveillance’ in the New York subway, using object and pattern-recognition algorithms to identify dangerous activities in video feeds, such as a person abandoning a package. By considering the necessary preconditions for computer vision systems to recognize patterns and objects, I show how smart surveillance was challenged by the lack of visual and social uniformities necessary for smart surveillance systems to make its fine-toothed distinctions. In spite of vast resources and involvement of a major military contractor, the project was eventually deemed a failure. Although problems in computer vision are being incrementally solved, those improvements do not yet add up to a holistic technology capable of parsing the realworld ambiguity of open-ended settings which do not meet the assumptions of the detection algorithms. In the absence of technologies that can handle the actual mess, the world itself must cooperate, but it often does not. The article demonstrates the importance of looking beyond the claims of technical efficacy in the study of security and surveillance to discover how technologies of inspection and control actually work, as a means to cut through the heavy rhetorical packaging in which they are sold to their publics. | Article discusses new smart surveilance technologies and highlight their limitations that need to be addressed before they can be fully relied on. | https://doi.org/10.1080/1369118X.2016.1260624 | ✓ | ✓ | ||||||||||||||||||||
53 | González, Prieto, L., Jaedicke, C., Schubert, J., & Stantchev, V. (2016). Fog computing architectures for healthcare. Journal of Information, Communication and Ethics in Society, 14(4), 334-349. | Structured abstract: Purpose – The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment. Design/methodology/approach – This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer). Findings – A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach. Practical implications – Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits. Originality/value: The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments. | Article explores a particular AI application within the healthcare profession. | https://doi.org/10.1108/JICES-05-2016-0014 | ✓ | ✓ | ✓ | |||||||||||||||||||
54 | 6, Perri. (2001). Ethics, regulation and the new artificial intelligence, Part I: Accountability and power. Information, Communication & Society, 4(2), 199-229. | A generation ago, there was a major debate about the social and ethical implications of artificial intelligence (AI). Interest in that debate waned from the late 1980s. However, both patterns of public risk perception and new technological developments suggest that it is time to re-open that debate. The important issues about AI arise in connection with the prospect of robotic and digital agent systems taking socially significant decisions autonomously. Now that this is possible, the key concerns are now about which decisions should be and which should not be delegated to machines, issues of regulation in the broad sense covering everything from consumer information through codes of professional ethics for designers to statutory controls, issues of design responsibility and problems of liability. | Article identifies the issues in decision-making related to AI, mainly what decisions should AI be entrusted with and how can responsibility and codes of ethics be ensured in AI. | https://doi.org/10.1080/713768525 | ✓ | ✓ | ||||||||||||||||||||
55 | 6, Perri. (2001). Ethics, regulation and the new artificial intelligence, Part II: Autonomy and liability. Information, Communication & Society, 4(3), 406-434. | This is the second article in a two-part series on the social, ethical and public policy implications of the new artificial intelligence (AI). The first article briefly presented a neo-Durkheimian understanding of the social fears projected onto AI, before arguing that the common and enduring myth of an AI takeover arising from the autonomous decision-making capability of AI systems, most recently resurrected by Professor Kevin Warwick, is misplaced. That article went on to argue that, nevertheless, some genuine and practical issues in the accountability of AI systems that must be addressed. This second article, drawing further on the neo-Durkheimian theory, sets out a more detailed understanding of what it is for a system to be autonomous enough in its decision making to blur the boundary between tool and agent. The importance of this is that this blurring of categories is often the basis, the first article argued, of social fears. | This article is a continuation of the previous item and builds on the previous discussion by discussing the issue of autonomy and liability. | https://www.tandfonline.com/doi/abs/10.1080/713768548 | ✓ | ✓ | ||||||||||||||||||||
56 | Székely, I., Dániel Szabó, M. & Vissy, B. (2011). Regulating the future? law, ethics, and emerging technologies. Journal of Information, Communication and Ethics in Society, 9(3), 180-194. | Purpose - The purpose of the paper is to provide an overview of the legal implications which may be relevant to the ethical aspects of emerging technologies, to explore the existing situation in the area of legal regulation at EU level, and to formulate recommendations for the lawmakers. Design/methodology/approach - The analysis is based on the premise that the law is supposed to invoke moral principles. Speculative findings are formulated on the basis of analyzing specific emerging technologies; empirical findings are based on a research conducted in the whole legal corpus of the EU. Findings - In the area of network-based technologies the already existing and elaborated legal frameworks can be used in an extended manner; artificial intelligence-based technologies call for alterations in several branches of law; while interface technologies show the difficulty and complexity of regulating interdisciplinary fields. The legal implications of emerging technologies have attracted only a minimal legislative attention in the competent bodies of the EU. Originality/value - The paper provides a systemic approach towards transmitting ethical norms to the application of emerging technologies through legal regulation, and formulates detailed recommendations in various areas of such technologies. | Article discusses technology related law-making and regulations; ethical implications of emerging technologies. | https://www.emerald.com/insight/content/doi/10.1108/14779961111167658/full/html | ✓ | ✓ | ✓ | |||||||||||||||||||
57 | Promta, S., & Einar Himma, K. (2008). Artificial intelligence in buddhist perspective. Journal of Information, Communication and Ethics in Society, 6(2), 172-187. | Structured Abstract: Purpose - The purpose of this paper is to explore the possibility and desirability of artificial intelligence (AI) by considering western literature on AI and Buddhist doctrine. Design/methodology/approach - The paper argues that these issues can best be considered examined from a variety of philosophical and religious viewpoints and that resolution of those issues depends on which point of view the questions are addressed from. There are a number of philosophical questions involving AI usually considered by philosophers: what is the definition of AI, what is a status of an AI as compared with human intelligence, is there a legitimate purpose for creating AI; if so, what is that purpose? Buddhism is a religion that is deeply philosophical and, perhaps to the surprise of western readers, has a lot to say about the nature of human mind and human intelligence. Although Buddhism does not talk explicitly about AI, the richness of its philosophical views concerning human nature and the nature of the physical world sheds considerable light on the philosophical questions stated above. Findings - The paper explains how Buddhist teaching would answer the four questions above. Originality/value - The paper is the first to clarify the Buddhist position on AI, and perhaps represents the first attempt to explore the relationships between any major religion and the AI agenda. | This article is not related to any particular profession but an interesting viewpoint on AI and highlights the importance of context and culture in studying and discussing AI and its impact on professions. | https://doi.org/10.1108/14779960810888374 | ✓ | |||||||||||||||||||||
58 | Pana, L. (2008). Artificial intelligence and moral intelligence. TripleC: Communication, Capitalism & Critique, 4(2), 254-264. | We discuss the thesis that the implementation of a moral code in the behaviour of artificial intelligent systems needs a specific form of human and artificial intelligence, not just an abstract intelligence. We present intelligence as a system with an internal structure and the structural levels of the moral system, as well as certain characteristics of artificial intelligent agents which can/must be treated as 1- individual entities (with a complex, specialized, autonomous or selfdetermined, even unpredictable conduct), 2- entities endowed with diverse or even multiple intelligence forms, like moral intelligence, 3- open and, even, free-conduct performing systems (with specific, flexible and heuristic mechanisms and procedures of decision), 4 – systems which are open to education, not just to instruction, 5- entities with “lifegraphy”, not just “stategraphy”, 6- equipped not just with automatisms but with beliefs (cognitive and affective complexes), 7- capable even of reflection (“moral life” is a form of spiritual, not just of conscious activity), 8 – elements/members of some real (corporal or virtual) community, 9 – cultural beings: free conduct gives cultural value to the action of a ”natural” or artificial being. Implementation of such characteristics does not necessarily suppose efforts to design, construct and educate machines like human beings. The human moral code is irremediably imperfect: it is a morality of preference, of accountability (not of responsibility) and a morality of non-liberty, which cannot be remedied by the invention of ethical systems, by the circulation of ideal values and by ethical (even computing) education. But such an imperfect morality needs perfect instruments for its implementation: applications of special logic fields; efficient psychological (theoretical and technical) attainments to endow the machine not just with intelligence, but with conscience and even spirit; comprehensive technical means for supplementing the objective decision with a subjective one. Machine ethics can/will be of the highest quality because it will be derived from the sciences, modelled by techniques and accomplished by technologies. If our theoretical hypothesis about a specific moral intelligence, necessary for the implementation of an artificial moral conduct, is correct, then some theoretical and technical issues appear, but the following working hypotheses are possible: structural, functional and behavioural. The future of human and/or artificial morality is to be anticipated. | Article explores the issue of morality and ethics in AI and argues that AI has the potential for a higher quality morality than humans, as former will be derived. | https://doi.org/10.31269/triplec.v4i2.43 | ✓ | |||||||||||||||||||||
59 | Weber, J. (2005). Helpless machines and true loving care givers: A feminist critique of recent trends in human-robot interaction. Journal of Information, Communication and Ethics in Society, 3(4), 209-218. | In recent developments in Artificial Intelligence (AI) and especially in robotics we can observe a tendency towards building intelligent artefacts that are meant to be social, to have ‘human social’ characteristics like emotions, the ability to conduct dialogue, to learn, to develop personality, character traits, and social competencies. Care, entertainment, pet and educational robots are conceptualised as friendly, understanding partners and credible assistants which communicate ‘naturally’ with users, show emotions and support them in everyday life. Social robots are often designed to interact physically, affectively and socially with humans in order to learn from them. To achieve this goal, roboticists often model the human-robot interaction on early caregiver-infant interactions. In this paper I want to analyse prominent visions of these ‘socio-emotional’ machines as well as early prototypes and commercial products with regard to the human-machine interface. By means of this I will ask how feminist critiques of technology could be applied to the field of social robotics in which concepts like sociality or emotion are crucial elements while, at the same time, these concepts play an important role in feminist critiques of technology. | Article applies femnist critiques of technology to argue the issue of emotional intelligence (EQ) and social intelligence (SQ) in human-robot interaction. | https://pdfs.semanticscholar.org/19a7/184535eeb1339f89318f2fad41ad3f0525d8.pdf | ✓ | |||||||||||||||||||||
60 | Miller, K., & Larson, D. (2005). Angels and artifacts: Moral agents in the age of computers and networks. Journal of Information, Communication and Ethics in Society, 3(3), 151-157. | Traditionally, philosophers have ascribed moral agency almost exclusively to humans (Eshleman, 2004). Early writing about moral agency can be traced to Aristotle (Louden, 1989) and Aquinas (1997). In addition to human moral agents, Aristotle discussed the possibility of moral agency of the Greek gods and Aquinas discussed the possibility of moral agency of angels. In the case of angels, a difficulty in ascribing moral agency was that it was suspected that angels did not have enough independence from God to ascribe to the angels genuine moral choices. Recently, new candidates have been suggested for non-human moral agency. Floridi and Sanders (2004) suggest that artificially intelligence (AI) programs that meet certain criteria may attain the status of moral agents; they suggest a redefinition of moral agency to clarify the relationship between artificial and human agents. Other philosophers, as well as scholars in Science and Technology Studies, are studying the possibility that artifacts that are not designed to mimic human intelligence still embody a kind of moral agency. For example, there has been a lively discussion about the moral intent and the consequential effects of speed bumps (Latour, 1994; Keulartz et al., 2004). The connections and distributed intelligence of a network is another candidate being considered for moral agency (Allen, Varner & Zinser, 2000). These philosophical arguments may have practical consequences for software developers, and for the people affected by computing. In this paper, we will examine ideas about artificial moral agency from the perspective of a software developer. | This article considers philosophical and spiritual views of morality from earlier thinkers and argues that it is possible that AI might develop an angel-like morality as it will be independent from the weaknesses humans are prone to. | https://doi.org/10.1108/14779960580000269 | ✓ | |||||||||||||||||||||
61 | Khalil, Omar E. M. (1993). Artificial decision-making and artificial ethics: A management concern. Journal of Business Ethics, 12(4), 313-321. | Expert systems are knowledge-based information systems which are expected to have human attributes in order to replicate human capacity in ethical decision making. An expert system functions by virtue of its information, its inferential rules, and its decision criteria, each of which may be problematic. Three basic reasons for ethical concern when using the currently available expert systems in a decision-making capacity are addressed. These reasons are: 1. expert systems' lack of human intelligence, 2. expert systems' lack of emotions, and 3. expert systems' possible incorporation of intentional or accidental bias. For these reasons, artificial ethics seems to be science fiction. Consequently, expert systems should be used only in an advising capacity, and managers should not absolve themselves from legal and ethical responsibility when using expert systems in decision making. | Article discusses the role of artificial intelligence (expert systems) in decision-making and highlights the need for sustaining legal and ethical responsibility in managerial and expert decision-making. | https://link.springer.com/article/10.1007/BF01666535 | ✓ | |||||||||||||||||||||
62 | Aguilar, J., Sánchez, M., Cordero, J., Valdiviezo-Díaz, P., Barba-Guamán, L., & Chamba-Eras, L. (2018). Learning analytics tasks as services in smart classrooms. Universal Access in the Information Society, 17(4), 693-709. | A smart classroom integrates the different components in a traditional classroom, by using different technologies as artificial intelligence, ubiquitous, and cloud paradigms, among others, in order to improve the learning process. On the other hand, the learning analytics tasks are a set of tools that can be used to collect and analyze the data accumulated in a smart classroom. In this paper, we propose the definition of the learning analytics tasks as services, which can be invoked by the components of a smart classroom. We describe how to combine the cloud and multi-agent paradigms in a smart classroom, in order to provide academic services to the intelligent and non-intelligent agents in the smart classroom, to adapt and respond to the teaching and learning requirements of students. Additionally, we define a set of learning analytics tasks as services, which defines a knowledge feedback loop for the smart classroom, in order to improve the learning process in it, and we explain how they can be invoked and consumed by the agents in a smart classroom. | Article discusses the concept of the smart classroom and indirectly shows how AI might lead to transformation in education and the teaching profession. | doi:10.1007/s10209-017-0525-0 | ✓ | ✓ | ||||||||||||||||||||
63 | Winter, J. S., & Davidson, E. (2019). Big data governance of personal health information and challenges to contextual integrity. The Information Society, 35(1), 36-51. | Pervasive digitization and aggregation of personal health information (PHI), along with artificial intelligence (AI) and other advanced analytical techniques, hold promise of improved health and healthcare services. These advances also pose significant data governance challenges for ensuring value for individual, organizational, and societal stakeholders as well as individual privacy and autonomy. Through a case study of a controversial public-private partnership between Royal Free Trust, a National Health Service hospital system in the United Kingdom, and Alphabet’s AI venture DeepMind Health, we investigate how forms of data governance were adapted, as PHI data flowed into new use contexts, to address concerns of contextual integrity, which is violated when personal information collected in one use context moves to another use context with different norms of appropriateness. | Article discusses the use and challenges of AI in the health profession related to big data governance of personal information. | https://doi.org/10.1080/01972243.2018.1542648 | ✓ | |||||||||||||||||||||
64 | Bloomfield, B. P., & Vurdubakis, T. (2008). IBM's chess players: On AI and its supplements. The Information Society, 24(2), 69-82. | This article investigates the ways in which the reporting of technological developments in artificial intelligence (AI) can serve as occasions in which Occidental modernity's cultural antinomies are played out. It takes as its reference point the two chess tournaments (in 1996 and 1997) between the then world champion Gary Kasparov and the IBM dedicated chess computers Deep Blue and Deeper Blue and shows how these games of chess came to be seen as an arena where fundamental issues pertaining to human identity were contested. The article considers the dominant framing of these encounters in terms of a conflict between two opposed categories—“human” and “machine”—and argues the essential role of human agency, the human supplement, in the performances of machine intelligence. | Article not entirely related to professions but shows an interesting phenomenon of AI in chess games which were once thought to be deeply embedded human skills and might signal similar prospects for other professions. | https://doi.org/10.1080/01972240701883922 | ✓ | |||||||||||||||||||||
65 | Birtchnell, T. (2018). Listening without ears: Artificial intelligence in audio mastering. Big Data & Society, 5(2): 1-16. | Since the inception of recorded music there has been a need for standards and reliability across sound formats and listening environments. The role of the audio mastering engineer is prestigious and akin to a craft expert combining scientific knowledge, musical learning, manual precision and skill, and an awareness of cultural fashions and creative labour. With the advent of algorithms, big data and machine learning, loosely termed artificial intelligence in this creative sector, there is now the possibility of automating human audio mastering processes and radically disrupting mastering careers. The emergence of dedicated products and services in artificial intelligence-driven audio mastering poses profound questions for the future of the music industry, already having faced significant challenges due to the digitalization of music over the past decades. The research reports on qualitative and ethnographic inquiry with audio mastering engineers on the automation of their expertise and the potential for artificial intelligence to augment or replace aspects of their workflows. Investigating audio mastering engineers' awareness of artificial intelligence, the research probes the importance of criticality in their labour. The research identifies intuitive performance and critical listening as areas where human ingenuity and communication pose problems for simulation. Affective labour disrupts speculation of algorithmic domination by highlighting the pragmatic strategies available for humans to adapt and augment digital technologies. | This article about AI in the sound industry is an interesting view of the role of AI in creative professions, which is rarely seen in the literature. | https://doi.org/10.1177/2053951718808553 | ✓ | |||||||||||||||||||||
66 | Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019). Artificial intelligence and the future of primary care: Exploratory qualitative study of UK general practitioners' views. Journal of Medical Internet Research, 21(3), e12802. | The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics and related fields. The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics and related fields. This study aimed to explore general practitioners’ (GPs’) opinions about the potential impact of future technology on key tasks in primary care. In June 2018, a Web-based survey of 720 UK GPs’ gathered opinions about the likelihood of future technology to fully replace GPs in performing 6 key primary care tasks, and, if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. Comments were classified into 3 major categories in relation to primary care: (1) limitations of future technology, (2) potential benefits of future technology, and (3) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning and the ability to provide value-based care as necessitating physicians’ judgments. Perceived benefits of technology included expectations about improved efficiencies, in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians. This study presents timely information on physicians’ views about the scope of artificial intelligence (AI) in primary care. Overwhelmingly, GPs considered the potential of AI to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs’ views. | n.a. | doi:10.2196/12802 | ✓ | |||||||||||||||||||||
67 | Ye, J. J. (2015). Artificial intelligence for pathologists is not near-it is here: Description of a prototype that can transform how we practice pathology tomorrow. Archives of Pathology and Laboratory Medicine, 139(7), 929-935. | Pathologists' daily tasks consist of both the professional interpretation of slides and the secretarial tasks of translating these interpretations into final pathology reports, the latter of which is a time-consuming endeavor for most pathologists. The research Objective was to describe an artificial intelligence that performs secretarial tasks, designated as Secretary-Mimicking Artificial Intelligence (SMILE). The underling implementation of SMILE is a collection of computer programs that work in concert to “listen to” the voice commands and to “watch for” the changes of windows caused by slide bar code scanning; SMILE responds to these inputs by acting upon PowerPath Client windows (Sunquest Information Systems, Tucson, Arizona) and its Microsoft Word (Microsoft, Redmond, Washington) Add-In window, eventuating in the reports being typed and finalized. Secretary-Mimicking Artificial Intelligence also communicates relevant information to the pathologist via the computer speakers and message box on the screen. Secretary-Mimicking Artificial Intelligence performs many secretarial tasks intelligently and semiautonomously, with rapidity and consistency, thus enabling pathologists to focus on slide interpretation, which results in a marked increase in productivity, decrease in errors, and reduction of stress in daily practice. Secretary-Mimicking Artificial Intelligence undergoes encounter-based learning continually, resulting in a continuous improvement in its knowledge-based intelligence. Artificial intelligence for pathologists is both feasible and powerful. The future widespread use of artificial intelligence in our profession is certainly going to transform how we practice pathology. | n.a. | doi:10.5858/arpa.2014-0478-OA | ✓ | ✓ | ✓ | |||||||||||||||||||
68 | Kortesniemi, M., Tsapaki, V., Trianni, A., Russo, P., Maas, A., Källman, H., Brambilla, M., & Damilakis, J. (2018). The european federation of organisations for medical physics (EFOMP) white paper: Big data and deep learning in medical imaging and in relation to medical physics profession. Physica Medica, 56, 90-93. | Big data and deep learning will profoundly change various areas of professions and research in the future. This will also happen in medicine and medical imaging in particular. As medical physicists, we should pursue beyond the concept of technical quality to extend our methodology and competence towards measuring and optimising the diagnostic value in terms of how it is connected to care outcome. Functional implementation of such methodology requires data processing utilities starting from data collection and management and culminating in the data analysis methods. Data quality control and validation are prerequisites for the deep learning application in order to provide reliable further analysis, classification, interpretation, probabilistic and predictive modelling from the vast heterogeneous big data. Challenges in practical data analytics relate to both horizontal and longitudinal analysis aspects. Quantitative aspects of data validation, quality control, physically meaningful measures, parameter connections and system modelling for the future artificial intelligence (AI) methods are positioned firmly in the field of Medical Physics profession. It is our interest to ensure that our professional education, continuous training and competence will follow this significant global development. | Article explores and projects how the use of big data and big data analytics will transform the medical profession, medical imaging in particular and how this will increase requirements for continuous education and training among healthcare professionals. | doi:10.1016/j.ejmp.2018.11.005 | ✓ | |||||||||||||||||||||
69 | Young, A. J. (2016). New technologies and general practice. British Journal of General Practice, 66(653), 601-602. | Objective: This study aimed to explore general practitioners’ (GPs’) opinions about the potential impact of future technology on key tasks in primary care. | Article discusses the role of AI in the medical profession, general practice in particular, together with some challenges it brings for the medical staff and management of chronic diseases. | https://bjgp.org/content/bjgp/66/653/601.full.pdf | ✓ | |||||||||||||||||||||
70 | Nissan, E. (2017). Digital technologies and artificial intelligence's present and foreseeable impact on lawyering, judging, policing and law enforcement. AI & Society, 32(3), 441-464. | `AI & Law' research has been around since the 1970s, even though with shifting emphasis. This is an overview of the contributions of digital technologies, both artificial intelligence and non-AI smart tools, to both the legal professions and the police. For example, we briefly consider text mining and case-automated summarization, tools supporting argumentation, tools concerning sentencing based on the technique of case-based reasoning, the role of abductive reasoning, research into applying AI to legal evidence, tools for fighting crime and tools for identification. | Article discusses how AI changes the reasoning traditionally used in the law profession. | 10.1007/s00146-015-0596-5 | ✓ | ✓ | ||||||||||||||||||||
71 | Kozjek, T., Rajkovic, V., & Ferjan, M. (2012). Decision model for the media selection. Uprava, 10(1), 145 - 165. | The article presents the decision model for choosing the media in the performance of public relations (PR). The core of the model lies in the use of modern information methods with the accent on the artificial intelligence methods in decision-making processes which allow transparency and simple explanation of the decision knowledge and of the decision itself. The framework of the solution is the description of the realization of the model with the support of the DEXi system. The model covers the qualitative and the quantitative measures for the final choice. This allows a more wholesome overview of the media and of the goals the decision aims to achieve. The determined fundamental elements of the model are: the target public, the goals the organization aims to achieve via public relations, the resources assigned to the public relations, the messages to transmit to the public, etc. The criteria which form constituent parts of the decision model have been designed on the basis of public relations literature study and the practical experience of the Faculty. | Article discusses the use of an AI-based model for media selection in the PR profession and the decision-making process it entails. | ✓ | ||||||||||||||||||||||
72 | Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69-80. | Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios. | Article discusses the use of AI and big data in farming in what is called a smart farming and shows how AI has transformed/is transforming agriculture and decision-making in agricultural businesses. | https://doi.org/10.1016/j.agsy.2017.01.023 | ✓ | |||||||||||||||||||||
73 | Instablogs (2017) Is your job safe from robots? (2017). Shimla: Athena Information Solutions Pvt. Ltd. | Learners who would take up finance and accounts profession in future stand in the most vulnerable corner with a high chance of machines replacing them as a faster and more methodically programmed substitute with no monthly pay cheques, neither any fringe benefits or any medical reimbursements. At this time, amid a rising worry about robots competing with the human beings and beating them to the punch at workstations, the potential candidates feel uneasy and intimidated whether their selected discipline would end up in extinction making their position and utility to the organization they work for redundant in future. The following jobholders are relatively insulated against the threat of future automation: * Solicitors. * Management consultants. * Journalists. * Public relations officers * Primary school teachers. * Sports science professionals. * Biologists. [...]development in artificial intelligence has gained momentum since the General Motor days back in 1961 when this mammoth organization. | n.a. | ✓ | ✓ | ✓ | ||||||||||||||||||||
74 | Aguilera, A., Ramos Barrera, M. G., Universidad de La Salle, & Universidad EAN. (2016). Technological unemployment: An approximation to the latin american case. AD-Minister, (29), 59-78. [article not available for download]. | Recent advancements in Artificial Intelligence (AI), robotics, control systems, software and related technologies have revived the debate on the influence that technology has on labor markets. So far, the focus of the literature has been on advanced economies. This document aims to analyze the following variables: domestic spending in science and technology, GDP per capita, nominal minimum wage, domestic spending in education and their impact on unemployment rate in seven Latin American countries from 1996 to 2011. Panel data was used to measure the relation of these variables with unemployment rates in the region. The results allowed us to conclude that investment in Science and Technology in the region has not reached levels that potentially reduce employment; on the contrary, innovation is regarded as a source of labor productivity gains. The broader implications of technology and automation are yet to be seen, however, both firms and the public sector in the region must prepare for massive technological unemployement, as predicted by recent models. | Article tests the impact of AI and automation on unemployment in Latin America concluding that unemployment is likely to rise massively due to technological enhancements. | doi:10.17230/ad-minister.29.3 | ✓ | ✓ | ✓ | |||||||||||||||||||
75 | Stamenković, S. (2015). Journalism and media future - creating identity and reality. Medias Res, 4(6), 838-858. | Media and journalism are in constant uncertainty caused by the influence of online communities . A certain change is: ways of content distribution to consumers, media content and its production. Changes induced by metamedia - the internet is changing the position of the media market and the fundamental role of journalism. In response to these challenges in the U.S. several years ago began to develop the concept of "entrepreneurial journalism" that is still relatively unknown in the European public. Computers write news for, and artificial-intelligence experts at Google, predict that by of 2029. PCs to be smarter than the people, that in the next 15 years become more and more intelligent and will be able to understand what you 're talking about, learn from experience, they joke, tell stories, and even flirt. Journalism still has not found the outlines of his new identity, but it partly determines the logic of speed, brevity and superficiality of competition. The role of the editor is changing drastically - the editor should develop a vision angle and create an environment of different specialists who will thoughtfully implement it in practice. To have a greater effect on advertisers, journalists and editors are expected to not act in isolation in relation to the media business, and they know the mechanisms that determine whether and how advertisers appear within the online media. Respecting editorial integrity, journalists are the ones who should influence the greater engagement of readers. New professions in mediasfera is the creator of Content Curation, which are of great use journalistic and editorial experience. Computer - mediated communication replaces the actual communication and the media no longer represent reality but create it. In such circumstances, the media are no longer defined by their monopolistic or oligopolistic status, but their position in the market is determined to create their own identity in the online environment without boundaries. Classic media are still in the process of seeking effective business model and profit model that will replace the models that no longer work. In the next few years, creating content as the area will be the backbone of the formation of the Company's budget for the three dependent areas - media, marketing and PR. Ignoring the changes in media and journalism that the developed world is driven only means that some media will not survive, that some journalists will lose their jobs or become preparers content. Changes as a permanent modus vivendi in Serbia and the region are visible through a change in the way the media and the changing structure media content, which is the subject of the research conducted for this study. To predict the future of media and journalism are needed facts in the absence of which it is possible to rely on the thinking and experience of developed media community. One thing is certain: journalism and the media will not function the way it was before. | Article discusses the role of AI in journalism, in particular content curation which is currently attributed to experts in this areas and predicts that in the next decade, robots and AI might become more intelligent than humans, thus substituting them in the journalism profession. | https://doaj.org/article/090254bc9ebd427b8ff567ca49ee648e | ✓ | ✓ | ||||||||||||||||||||
76 | Ford, M. (2013). Could artificial intelligence create an unemployment crisis? Communications of the ACM, 56(7), 37-39. | Reflective piece on the nature of the transistion in workforce skills that will be required because of AI. Removal of routine work i.e. that which can be broken down into discrete tasks is transformational and different in the AI revolution. Few jobs are truely purely creative. | doi:10.1145/2483852.2483865 | ✓ | ✓ | ✓ | ||||||||||||||||||||
77 | Walsh, T. (2018). Expert and non-expert opinion about technological unemployment. International Journal of Automation and Computing, 15(5), 637-642. | There is significant concern that technological advances, especially in robotics and artificial intelligence (AI), could lead to high levels of unemployment in the coming decades. Studies have estimated that around half of all current jobs are at risk of automation. To look into this issue in more depth, we surveyed experts in robotics and AI about the risk, and compared their views with those of non-experts. Whilst the experts predicted a significant number of occupations were at risk of automation in the next two decades, they were more cautious than people outside the field in predicting occupations at risk. Their predictions were consistent with their estimates for when computers might be expected to reach human level performance across a wide range of skills. These estimates were typically decades later than those of the non-experts. Technological barriers may therefore provide society with more time to prepare for an automated future than the public fear. In addition, public expectations may need to be dampened about the speed of progress to be expected in robotics and AI. | Articles provides a comparison of expert and non-expert views on technology and AI induced unemployment with experts suggesting that technological development might take more time than most non-experts expect. | https://link.springer.com/article/10.1007/s11633-018-1127-x | ✓ | |||||||||||||||||||||
78 | Chomanski, B. (2018). Massive technological unemployment without redistribution: A case for cautious optimism. Science and Engineering Ethics, 25(5), 1389-1407. | This paper argues that even though massive technological unemployment will likely be one of the results of automation, we will not need to institute mass-scale redistribution of wealth (such as would be involved in, e.g., instituting universal basic income) to deal with its consequences. Instead, reasons are given for cautious optimism about the standards of living the newly unemployed workers may expect in the (almost) fully-automated future. It is not claimed that these predictions will certainly bear out. Rather, they are no less likely to come to fruition than the predictions of those authors who predict that massive technological unemployment will lead to the suffering of the masses on such a scale that significant redistributive policies will have to be instituted to alleviate it. Additionally, the paper challenges the idea that the existence of a moral obligation to help the victims of massive unemployment justifies the coercive taking of anyone else’s property. | Article focuses on technological unemployment which might bring about changes related to redistribution of wealth, although this is not explicitly discussed in the article there might be changes incured for social work and governance related professions. | 10.1007/s11948-018-0070-0 | ✓ | |||||||||||||||||||||
79 | Pol, E., & Reveley, J. (2017). Robot induced technological unemployment: Towards a youth-focused coping strategy. Psychosociological Issues in Human Resource Management, 5(2), 169-186. | As an agent of economic and social change, robotization has elicited considerable concern about technological unemployment. Focusing on youth, this paper makes four contributions to the debate over this labour-displacing technological change's effects. First, to clarify the magnitude of the job threat to young people, the paper accentuates the conceptual distinction between technological unemployment and frictional unemployment. Second, the possibility of persistent technological unemployment, which the young are currently facing, is linked to strong uncertainty stemming from the rapidity of invention in robotics and artificial intelligence. Third, the paper advances a plausibility-based argument about the inevitability of technological unemployment. Fourth, coping behaviour is shown to be logically compatible with rationality and well-suited to dealing with fear of joblessness. Fifth, to the extent that robotization threatens future jobs, we maintain that coping strategies are needed to help members of the younger generation. A resilience-based strategy is suggested but we believe that there may be other coping strategies complementary to our proposal. | Article discusses the issue of youth unemployment due to robotisation. | https://scholars.uow.edu.au/display/publication117240 | ✓ | |||||||||||||||||||||
80 | Chrisinger, D. (2019). The solution lies in education: Artificial intelligence & the skills gap. On the Horizon, 27(1), 1-4. | Artificial intelligence (AI) holds substantial promise not only for improving American economic competitiveness in a variety of capacities but also for helping address some of society’s most pressing challenges. At the same time, however, AI will likely displace workers in some sectors and may exacerbate socioeconomic inequality. Of those who are paying attention to the advances in technology and the transformations those technologies are prompting, most are worried. According to a recent survey conducted by Northeastern University, 73 per cent of American workers believe that AI will eliminate more jobs than it creates (St Martin, 2018). And only 22 per cent of American workers with a bachelor’s degree or a higher level of education reported that their college or university prepared them well or very well to work with AI. To gain a better understanding of the implications resulting from developments in AI, the head of the USA Government Accountability Office convened a forum on AI, which was held in July 2017, in Washington, D.C. At the forum, 23 expert participants from private industry, government, academia and nonprofit organizations considered not only the potential implications of AI developments but also the policy implications of broadening the use of AI in the economy and society. | n.a. | 10.1108/OTH-03-2019-096 | ✓ | |||||||||||||||||||||
81 | Bruun, E. P. G., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13(2), 1-15. | Artificial intelligence is rapidly entering our daily lives in the form of driverless cars, automated online assistants and virtual reality experiences. In so doing, AI has already substituted human employment in areas that were previously thought to be uncomputerizable. Based on current trends, the technological displacement of labor is predicted to be significant in the future – if left unchecked this will lead to catastrophic societal unemployment levels. This paper presents a means to mitigate future technological unemployment through the introduction of a Basic Income scheme, accompanied by reforms in school curricula and retraining programs. Our proposal argues that such a scheme can be funded by a special tax on those industries that make use of robotic labour; it includes a practical roadmap that would see a government take this proposal from the conceptual phase and implement it nationwide in the span of one decade. | Paper highlights the issue of potential AI induced unemployment and presents possible ways of overcoming this problem. | doi:10.1515/bis-2018-0018 | ✓ | |||||||||||||||||||||
82 | Frank, M.R., Autor, D., Bessen, J.E., Brynjolfsson, E., Cebrian, M., Deming, D.J., Feldman, M., Groh, M., Lobo, J., Moro, E., Wang, D., Youn, H., & Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. PNAS, 116(14): 6531-6539. | Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). | The paper suggest developing a decision-framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior. | https://doi.org/10.1073/pnas.1900949116 | ✓ | ✓ | ||||||||||||||||||||
83 | Chiacchio, F., Petropoulos, G., & Pichler, D. (2018). The impact of industrial robots on EU employment and wages: A local labour market approach. Bruegel, Working Paper, Issue 02. | We study the impact of industrial robots on employment and wages in six European Union countries, that make up 85.5 percent of the EU industrial robots market. In theory, robots can directly displace workers from performing specific tasks (displacement effect). But they can also expand labour demand through the efficiencies they bring to industrial production (productivity effect). We adopt the local labour market equilibrium approach developed by Acemoglu and Restrepo (2017) to assess which of the two labour market effects dominates. We find that one additional robot per thousand workers reduces the employment rate by 0.16-0.20 percentage points. Thus a significant displacement effect dominates. We find that the displacement effect is particularly evident for workers of middle education and for young cohorts. Our estimates, however, do not point to robust and significant results on the impact of robots on wage growth, even after accounting for possible offsetting effects across different populations and sectoral groups. | Focuses on industrial robots as a factor of change in local labour markets - suggests a huge displacement effect. Most affected are young workers and ones with medium (secondary) education. Also, suggests a different effect due to ICT capital. | http://bruegel.org/wp-content/uploads/2018/04/Working-Paper-AB_25042018.pdf | ✓ | ✓ | ✓ | |||||||||||||||||||
84 | Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol., 2(4): 230-243. | Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI. | Looks at AI in healthcare and analyses two major categories of AI devices and AI application in stroke care. Points towards the need for regulations to assess AI system. | https://svn.bmj.com/content/2/4/230 | ✓ | ✓ | ✓ | |||||||||||||||||||
85 | Parry, E., & Battista, V. (2019). The impact of emerging technologies on work: a review of the evidence and implications for the human resource function. Emerald Open Research 2019, 1:5. | Popular media suggests that technological advancement will continue to have a dramatic effect on work, but it is difficult to distinguish between the hype surrounding this and the actual potential impacts. This study examines what the evidence is in relation to the impact of emerging technologies on work and the role of the human resource (HR) function in helping employees and organisations to navigate these changes. Evidence suggests that the latest technologies, such as artificial intelligence and robotics, are being employed by organisations to automate simple and repetitive tasks as well as to make complex decisions quickly and more accurately via predictive algorithms. In addition, emerging technologies are increasingly being used to support the implementation of more flexible working practices such as virtual work and gig work. However, this will present a number of challenges for HR professionals, who will need to help employees to update their skills to compete in the future world of work, and to find ways to address the possible negative effects of increased connectivity and precarious working arrangements on employee wellbeing. | Key research question addressed: what is the published evidence relating to the impact of emerging technologies on work and what is the role of HR in relation to these changes? | https://doi.org/10.12688/emeraldopenres.12907.1 | ✓ | ✓ | ||||||||||||||||||||
86 | Todd Schneider, T., Hong, G.H., & Van Le, A. (2018). Land of the Rising Robots. Finance & Development, 55(2): 28-31. | While automation will eliminate very few occupations entirely in the coming decades, it is likely to have an impact on portions of almost all jobs to some degree—depending on the type of work and the tasks involved. Set to move beyond routine and repetitive manufacturing activities, automation has the potential to appear in a much broader range of activities than seen until now, and to redefine human labor and work style in services and other sectors. In Japan, the rapid decline in the labor force and the limited influx of immigrants create a powerful incentive for automation, which makes the country a particularly useful laboratory for the study of the future landscape of work. | Discusses the role of AI in a range of industries beyond manufacturing. Uses Japan as an inetersting case given the decline in the labour-force and the lack of immigration. | https://www.imf.org/external/pubs/ft/fandd/2018/06/japan-labor-force-artificial-intelligence-and-robots/schneider.htm | ✓ | ✓ | ||||||||||||||||||||
87 | Francesc, P., Subosa, M., Rivas, A., & Paula, V. (2019). Artificial intelligence in education: challenges and opportunities for sustainable development. UNESCO, Working Paper on Education Policy, ED-2019/ WS / 8. | Artificial Intelligence is a booming technological domain capable of altering every aspect of our social interactions. In education, AI has begun producing new teaching and learning solutions that are now undergoing testing in different contexts. This working paper, written for education policymakers, anticipates the extent to which AI affects the education sector to allow for informed and appropriate policy responses. This paper gathers examples of the introduction of AI in education worldwide, particularly in developing countries, discussions in the context of the 2019 Mobile Learning Week and beyond, as part of the multiple ways to accomplish Sustainable Development Goal 4, which strives for equitable, quality education for all. First, this paper analyses how AI can be used to improve learning outcomes, presenting examples of how AI technology can help education systems use data to improve educational equity and quality in the developing world. Next, the paper explores the different means by which governments and educational institutions are rethinking and reworking educational programmes to prepare learners for the increasing presence of AI in all aspects of human activity. The paper then addresses the challenges and policy implications that should be part of the global and local conversations regarding the possibilities and risks of introducing AI in education and preparing students for an AI-powered context. Finally, this paper reflects on future directions for AI in education, ending with an open invitation to create new discussions around the uses, possibilities and risks of AI in education for sustainable development. | The objective of this paper is to identify policy implications of AI in the context of education. Also suggests promoting skill development for the learners to strive in AI driven future. | https://unesdoc.unesco.org/ark:/48223/pf0000366994 | ✓ | ✓ | ||||||||||||||||||||
88 | Silva, E A (2004). The DNA of our regions: artificial inteeligence in regional planning. Futures, 36(10), 1077-1094. | Researchers frequent concern with the separation between the computational modeling field and the development of theories is the starting point of this paper main claim that new Artificial Intelligence fields such as Artificial Life and Cellular Automaton can unite both areas (theory and model development), by defining data-led theory. To support this claim this paper stresses that it is possible to define a regional DNA1 through the use of CA mod- els, and by doing so contribute to the development of theory. First the main historical pha- ses of the computer model simulations in planning are presented. Second, the reasons why CAs are sensitive to local conditions and why that is very useful both as descriptive and pre- scriptive tool in planning (and in its tacit application) are detailed. Finally, it explores how the use of these calibration values can have another function by identifying keys/DNAs of each region. The use of two case studies points to the fact that it is indeed possible to define a regional DNA. Applications go beyond purely descriptive elements; they might have some- thing to say in terms of data-led theories. Unlike past theories, they need to reflect an increasingly complex world, but keep with a simplicity that makes them very appealing. | The paper explores the unrealised opportunities relating to AI in regional planning. | https://www.sciencedirect.com/science/article/pii/S0016328704000655 | ✓ | |||||||||||||||||||||
89 | Makridakis, S (2017). The forthcoming artificial intelligence (AI) revolution: its impact on society and firms. Futures, 90, 46-60. | The impact of the industrial and digital (information) revolutions has, undoubtedly, been substantial on practically all aspects of our society, life, firms and employment. Will the forthcoming AI revolution produce similar, far-reaching effects? By examining analogous inventions of the industrial, digital and AI revolutions, this article claims that the latter is on target and that it would bring extensive changes that will also affect all aspects of our society and life. In addition, its impact on firms and employment will be considerable, resulting in richly interconnected organizations with decision making based on the analysis and exploitation of “big” data and intensified, global competition among firms. People will be capable of buying goods and obtaining services from anywhere in the world using the Internet, and exploiting the unlimited, additional benefits that will open through the widespread usage of AI inventions. The paper concludes that significant competitive advantages will continue to accrue to those utilizing the Internet widely and willing to take entrepreneurial risks in order to turn innovative products/services into worldwide commercial success stories. The greatest challenge facing societies and firms would be utilizing the benefits of availing AI technologies, providing vast opportunities for both new products/services and immense productivity improvements while avoiding the dangers and disadvantages in terms of increased unemployment and greater wealth inequalities. | The paper suggests substantial uncertainity about the future impact of AI and points towards what can be done to maximise benefits and reduce costs. | https://edisciplinas.usp.br/pluginfile.php/4310583/mod_resource/content/1/The%20forthcoming%20Artificial%20Intelligence%20%28AI%29%20revolution_%20Its%20impact%20on%20society%20and%20firms.pdf | ✓ | ✓ | ||||||||||||||||||||
90 | Thompson, R.F., Valdes, G., Fuller, C.D., Carpenter, C.M., Morin, O., Aneja, S., Lindsay, W.D., J.W.L. Aerts, H., Agrimson, B., Deville Jr., C., Rosenthal, S.A., Yu, J.B., & Thomas Jr., C.R. (2018). Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiotherapy and Oncology, 129(3), 421-426. | Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully auton- omous transportation. Advances in each of these domains have led some to call AI the ‘‘fourth” industrial revolution. In healthcare, AI is emerging as both a productive and disruptive force across many disci- plines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine. | The article stresses that AI will change the future of medicine, and so there is a need to ensure optimal utilisation of talent, training and investment to ensure maximum potential use of AI. | https://www.thegreenjournal.com/article/S0167-8140(18)30289-5/abstract | ✓ | |||||||||||||||||||||
91 | Duana, Y., Edwards, J.S., & Dwivedi, Y.K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. | Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation. | n.a | https://doi.org/10.1016/j.ijinfomgt.2019.01.021 | ✓ | |||||||||||||||||||||
92 | Sniecinskia, I., & Seghatchian, J. (2018). Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfusion and Apheresis Science, 57(3), 422-424. | Artificial Intelligence (AI) reflects the intelligence exhibited by machines and software. It is a highly desirable academic field of many current fields of studies. Leading AI researchers describe the field as “the study and design of intelligent agents”. McCarthy invented this term in 1955 and defined it as “the science and engineering of making intelligent machines”. The central goals of AI research are reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. In fact the multidisplinary AI field is considered to be rather interdisciplinary covering numerous number of sciences and professions, including computer science, psychology, linguistics, philosophy and neurosciences. The field was founded on the claim that a central intellectual property of humans, intelligence-the sapience of Homo Sapiens “can be so precisely described that a machine can be made to simulate it”. This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings endowed with human-like in- telligence. Artificial Intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. The goal of this narrative is to review the potential use of AI approaches and their integration into pediatric cellular therapies and regenerative medicine. Emphasis is placed on recognition and application of AI techniques in the development of predictive models for personalized treatments with engineered stem cells, immune cells and regenerated tissues in adults and children. These intelligent machines could dissect the whole genome and isolate the immune particularities of individual patient’s disease in a matter of minutes and create the treatment that is customized to patient’s genetic specificity and immune system capability. AI techniques could be used for optimization of clinical trials of innovative stem cell and gene therapies in pediatric patients by precise planning of treatments, predicting clinical outcomes, simplifying recruitment and retention of patients, learning from input data and applying to new data, thus lowering their complexity and costs. Complementing human in- telligence with machine intelligence could have an exponentially high impact on continual progress in many fields of pediatrics. However how long before we could see the real impact still remains the big question. The most pertinent question that remains to be answered therefore, is can AI effectively and accurately predict properties of newer DDR strategies? The goal of this article is to review the use of AI method for cellular therapy and regenerative medicine and emphasize its potential to further the progress in these fields of medicine. | n.a | https://www.trasci.com/article/S1473-0502(18)30172-1/abstract | ✓ | |||||||||||||||||||||
93 | Goksel-Canbek, N., & Mutlu, M.E. (2016). On the track of Artificial Intelligence: Learning with Intelligent Personal Assistants. Journal of Human Sciences, 13(1), 592-601. | In a technology dominated world, useful and timely information can be accessed quickly via Intelligent Personal Assistants (IPAs). By the use of these assistants built into mobile operating systems, daily electronic tasks of a user can be accomplished 24/7. Such tasks like taking dictation, getting turn-by-turn directions, vocalizing email messages, reminding daily appointments, setting reminders, responding any factual questions and invoking apps can be completed by IPAs such as Apple’s Siri, Google Now and Microsoft Cortana. The mentioned assistants programmed within Artificial Intelligence (AI) do create an interaction between human and computer through a natural language used in digital communication. In this regard, the overall purpose of this study is to examine the potential use of IPAs that use advanced cognitive computing technologies and Natural Language Processing (NLP) for learning. To achieve this purpose, the working system of IPAs is reviewed briefly within the scope of AI that has recently become smarter to predict, comprehend and carry out multi-step and complex requests of users. | n.a | https://www.j-humansciences.com/ojs/index.php/IJHS/article/view/3549 | ✓ | ✓ | ||||||||||||||||||||
94 | Issa, H., Sun, T., & Vasarhelyi, M.A. (2016). Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1-20. | After decades of frustration with long ‘‘AI Winters,’’ various business industries are witnessing the arrival of AI’s ‘‘Spring,’’ with its massive and compelling benefits. Auditing will also evolve with the application of AI. Recently, there has been a progressive evolution of technology aimed at creating ‘‘artificially intelligent’’ devices. Although this evolution has been permeated with false starts and exaggerated claims, there is some convergence on the fact that substantive progress has been obtained in the last few years with the adoption of deep learning in conjunction with much faster machines and dimensionally larger storage spaces (and samples). The area of auditing has lagged business adoption in the past (Oldhouser 2016), but is prime for partial automation due to its labor intensiveness and range of decision structures. Several accounting firms have disclosed substantive investments in the AI fields. This paper proposes various areas of AI-related research to examine where this emerging technology is most promising. Moreover, this paper raises a series of methodological and evolutionary research questions aiming to study the AI-driven transformation of today’s world of audit into the assurance of the future. | The paper claims the AI as a potentially valuable tool for big data analytics, but also discusses vulnerability of professions such as auditing. | https://aaapubs.org/doi/10.2308/jeta-10511 | ✓ | ✓ | ||||||||||||||||||||
95 | Wartman, S.A., & Combs, C.D. (2018). Medical Education Must Move From the Information Age to the Age of Artificial Intelligence. Acad Med., 93(8), 1107-1109. | Noteworthy changes coming to the practice of medicine require significant medical education reforms. While proposals for such reforms abound, they are insufficient because they do not adequately address the most fundamental change—the practice of medicine is rapidly transitioning from the information age to the age of artificial intelligence. Increasingly, future medical practice will be characterized by: the delivery of care wherever the patient happens to be; the provision of care by newly constituted health care teams; the use of a growing array of data from multiple sources and artificial intelligence applications; and the skillful management of the interface between medicine and machines. To be effective in this environment, physicians must work at the top of their license, have knowledge spanning the health professions and care continuum, effectively leverage data platforms, focus on analyzing outcomes and improving performance, and communicate the meaning of the probabilities generated by massive amounts of data to patients, given their unique human complexities. The authors believe that a “reboot” of medical education is required that makes better use of the findings of cognitive psychology and pays more attention to the alignment of humans and machines in education and practice. Medical education needs to move beyond the foundational biomedical and clinical sciences. Systematic curricular attention must focus on the organization of professional effort among health professionals, the use of intelligence tools involving large data sets, and machine learning and robots, all the while assuring the mastery of compassionate care. | n.a | https://www.ncbi.nlm.nih.gov/pubmed/29095704 | ✓ | ✓ | ||||||||||||||||||||
96 | Share, P., & Pender, J. (2018). Preparing for a Robot Future? Social Professions, Social Robotics and the Challenges Ahead. Irish Journal of Applied Social Studies, 18(1), 45-62. | There have been significant developments in social robotics in the care sector: in particular, in the fields of elder care and in the care and education of children and young people, especially those with specific disabling conditions such as autism. Within the context of an increased interest in the social and ethical impact of automation and robotics, this paper, based on an overview of relevant literature, addresses some pertinent issues in the field of social robotics, social care and the social professions. It opens by pointing to the increasing application of information technologies in the care field, in areas such as Assistive Technology (AT) and Assistive Learning Technology (ALT). It defines social robotics and provides examples of relevant initiatives, prototypes and products in the care field, including and up to ‘third generation’ social robots. It outlines some of the challenges, in terms of ethics, human-robot relationships, acceptance and user-centred design, social impacts and issues of autonomy and control, and points to concerns about the professional identity of care providers. It identifies the need to respond in terms of educational/CPD supports and regulatory standards, for example in relation to the Irish regulators QQI and Coru. Finally, it suggests some pedagogical approaches to these issues that may be of assistance to educators, CPD practitioners and students within the social professions. | The paper discusses salient issues relating to social robotics and future of social professions. It suggests a number of pedagogical tactics to learn and teach social robotics to address those issues. | https://arrow.dit.ie/ijass/vol18/iss1/4 | ✓ | ✓ | ✓ | ✓ | ||||||||||||||||||
97 | Brougham, D., & Haar, J. (2018). Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257. | Futurists predict that a third of jobs that exist today could be taken by Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) by 2025. However, very little is known about how employees perceive these technological advancements in regards to their own jobs and careers, and how they are preparing for these potential changes. A new measure (STARA awareness) was created for this study that captures the extent to which employees feel their job could be replaced by these types of technology. Due to career progression and technology knowledge associated with age, we also tested age as a moderator of STARA. Using a mixed-methods approach on 120 employees, we tested STARA awareness on a range of job and well-being outcomes. Greater STARA awareness was negatively related to organisational commitment and career satisfaction, and positively related to turnover intentions, cynicism, and depression. | Discusses the idea of new industrial revolution and how employees view their future jobs and how they can prepare for potential changes. | https://doi.org/10.1017/jmo.2016.55 | ✓ | |||||||||||||||||||||
98 | Bourne, C. (2019). AI Cheerleaders: public relations, neo-liberalism and artificial Intelligence. Public Relations Inquiry, 8(2), 109 - 125. | Public relations’ (PR) professional habitus is defined by a relentless focus on optimism and futurity. This professional habitus renders PR indispensable to the corporate world after crisis, when new, potentially controversial, growth strategies must be sold-in to stakeholders. This article argues that PR’s professional habitus is heavily influenced by neoliberalism, an ideology which ‘confidently identifies itself with the future’. The discussion is timely, as 21st-century neoliberal capitalism becomes redefined by artificial intelligence (AI). The article combines PR theory, communications theory and political economy to consider the changing shape of neoliberal capitalism, as AI becomes naturalised as ‘common sense’ and a ‘public good’. The article explores how PR supports AI discourses, including promoting AI in national competitiveness and promoting ‘friendly’ AI to consumers, while promoting Internet inequalities. The article concludes that the PR profession’s myopia regarding the implications of promoting AI and neoliberalism is shaped by poor levels of diversity in the PR profession. | https://doi.org/10.1177/2046147X19835250 | ✓ | ✓ | ✓ | ||||||||||||||||||||
99 | Valentina, C., Romenti, S., Murtarelli, G. & Pizzetti, M. (2018). Digital visual engagement: influencing purchase intentions on Instagram. Journal of Communication Management, 22(4), 382 - 381. | Purpose – The purpose of this paper is to investigate the effects of visual communications on Instagram users’ propensity to engage with image-based content through online behaviors such as liking, sharing, commenting and following, and their intention to purchase the product depicted in the visual communications. Design/methodology/approach – An experimental design was used to measure the effect of branded Instagram images on a sample of active Instagram users. Two features of Instagram images (subject’s gaze: direct vs indirect; product salience: low vs high) were manipulated and their interactive effect tested on online behaviors. Findings – The paper offers empirical evidence that direct gaze and high product salience positively affect digital visual engagement. Moreover, digital visual engagement influences intention to purchase. Research limitations/implications – The hypotheses were tested on a single product category and on only two image-based features. Further studies might replicate the experiment on different product categories and include different image-based features. Practical implications – This empirical study can offer communication managers important information on the image-based features that are most effective in increasing digital visual engagement and positively influencing purchase intentions in visual communications. Originality/value – The study empirically demonstrates that the choice of specific image-based features in visual communication matters for increasing digital visual engagement among Instagram users. | DOI 10.1108/JCOM-01-2018-0005 | ✓ | ||||||||||||||||||||||
100 | Omoteso, K. (2012). The application of artificial intelligence in auditing: looking back to the future. Expert Systems with Applications, 39(9), 8490-8495. | ICT-based decision aids are currently making waves in the modern business world simultaneously with increased pressure on auditors to play a more effective role in the governance and control of corporate entities. This paper aims to review the main research efforts and current debates on auditors' use of artificial intelligent systems, with a view to predicting future directions of research and software development in the area. The paper maps the development process of artificial intelligent systems in auditing in the light of their identified benefits and drawbacks. It also reviews previous research efforts on the use of expert systems and neural networks in auditing and the implications thereof. The synthesis of these previous studies revealed certain research vacuum which future studies in the area could fill. Such areas include matching the benefits of adopting these intelligent agents with their costs, assessing the impact of artificial intelligence on internal control systems' design and monitoring as well as audit committees' effectiveness, and implications of using such systems for small and medium audit firms' operations and survival, audit education, public sector organisations' audit, auditor independence and audit expectations-performance gap. | http://dx.doi.org/10.1016/j.eswa.2012.01.098 | ✓ |