Examining the Advantages of Artificial Intelligence Alongside Its Potential Risks on Human Wellbeing, Data Privacy, and National Security
Keywords:artificial intelligence, benefits of AI, AI risks on human well-being, AI risks on data privacy, AI risks on national security, semi-systematic meta-narrative review
This study seeks to comprehensively analyze the benefits and risks of artificial intelligence and discuss strategies and policies to balance them. The paper assesses AI's positive impact on four industries - healthcare, finance, transportation, and education – juxtaposed with its negative welfare, privacy, and security effects. The study utilizes a semi-systematic review methodology to explore diverse narratives surrounding AI's societal implications. Key findings suggest AI can improve decision-making, productivity, and quality of life but risks exacerbating bias, unemployment, and insecurity if not developed responsibly. The paper discusses practical strategies, policies, and regulatory interventions to help balance AI's pros and cons, including human-centered design, explainable AI, and governance frameworks. It also suggests actionable recommendations for individual, organizational, and national stakeholders. Suggestions for future research include developing robust AI resilient to attacks, increasing AI transparency and accountability, assessing long-term societal impacts, and addressing legal and ethical dilemmas. This timely study contributes a measured perspective to current debates on AI and provides a framework to help appropriate its advantages while mitigating its perils.
J. F. Allen, “AI Growing Up: The Changes and Opportunities,” Ai Magazine, vol. 19, no. 4, pp. 13–23, Dec. 1998, doi: 10.1609/aimag.v19i4.1422.
S. Bhatnagar et al., “Mapping Intelligence: requirements and possibilities,” in Studies in applied philosophy, epistemology and rational ethics, 2018, pp. 117–135. doi: 10.1007/978-3-319-96448-5_13.
R. J. Brachman, “AA)AI More than the Sum of Its Parts,” Ai Magazine, vol. 27, no. 4, pp. 19–34, Dec. 2006, doi: 10.1609/aimag.v27i4.1907.
N. J. Nilsson, The quest for artificial intelligence. 2009. doi: 10.1017/cbo9780511819346.
S. J. Russell, P. Norvig, and E. Davis, Artificial intelligence: A Modern Approach. Prentice Hall, 2010.
R. Kurzweil, The age of intelligent machines. Cambridge, Mass.?: MIT Press, 1992.
E. Rich and K. Knight, Artificial intelligence. 1991.
J. Haugeland, Artificial intelligence: The Very Idea. MIT Press, 1989.
R. Bellman, An Introduction to Artificial intelligence: Can Computers think? 1978. [Online]. Available: https://ci.nii.ac.jp/ncid/BA03493655
E. Charniak, D. McDermott, and D. V. McDermott, Introduction to artificial Intelligence. Addison Wesley Publishing Company, 1985.
P. H. Winston, Artificial intelligence. Addison-Wesley, 1992.
N. J. Nilsson, Artificial intelligence: A New Synthesis. Morgan Kaufmann, 1998.
D. I. Poole et al., Computational intelligence: A Logical Approach. Oxford University Press on Demand, 1998.
G. Simons and D. S. Baldwin, “A critical review of the definition of ‘wellbeing’ for doctors and their patients in a post Covid-19 era,” International Journal of Social Psychiatry, vol. 67, no. 8, pp. 984–991, Jul. 2021, doi: 10.1177/00207640211032259.
M. Buckbee, “Data Privacy Guide: Definitions, Explanations and Legislation,” Varonis blog., Jun. 02, 2023. https://www.varonis.com/blog/data-privacy (accessed Oct. 20, 2023).
S. Osisanya, “National Security versus Global Security | United Nations,” United Nations. https://www.un.org/en/chronicle/article/national-security-versus-global-security (accessed Oct. 20, 2023).
O. Shani, “From science fiction to reality: the evolution of Artificial intelligence,” WIRED, Aug. 07, 2015. Accessed: Oct. 20, 2023. [Online]. Available: https://www.wired.com/insights/2015/01/the-evolution-of-artificial-intelligence/
S. Bianchini, M. Müller, and P. Pelletier, “Artificial intelligence in science: An emerging general method of invention,” Research Policy, vol. 51, no. 10, p. 104604, Dec. 2022, doi: 10.1016/j.respol.2022.104604.
A. Clyde, “AI for science and global citizens,” Patterns, vol. 3, no. 2, p. 100446, Feb. 2022, doi: 10.1016/j.patter.2022.100446.
M. Krenn et al., “On scientific understanding with artificial intelligence,” Nature Reviews Physics, vol. 4, no. 12, pp. 761–769, Oct. 2022, doi: 10.1038/s42254-022-00518-3.
Y. Xu et al., “Artificial intelligence: A powerful paradigm for scientific research,” The Innovation, vol. 2, no. 4, p. 100179, Nov. 2021, doi: 10.1016/j.xinn.2021.100179.
C. Chan, J. Moore, C. Chan, and J. Moore, “Generative AI: the next consumer platform,” Andreessen Horowitz, Sep. 2023, [Online]. Available: https://a16z.com/2023/02/07/everyday-ai-consumer/
W. Lehmacher, “5 reasons consumers will embrace artificial intelligence,” World Economic Forum, Jan. 04, 2018. https://www.weforum.org/agenda/2018/01/consumers-will-embrace-artificial-intelligence/ (accessed Oct. 20, 2023).
S. Puntoni, R. W. Reczek, M. Giesler, and S. Botti, “Consumers and Artificial intelligence: An Experiential perspective,” Journal of Marketing, vol. 85, no. 1, pp. 131–151, Oct. 2020, doi: 10.1177/0022242920953847.
T. H. Davenport, “3 things AI can already do for your company,” Harvard Business Review, Sep. 15, 2023. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
Forbes Technology Council, “15 Tech Experts Share Potential Impacts Of AI On Society,” Forbes, Jun. 26, 2020. Accessed: Oct. 20, 2023. [Online]. Available: https://www.forbes.com/sites/forbestechcouncil/2020/06/26/15-tech-experts-share-potential-impacts-of-ai-on-society/?sh=49b02bce3714
T. Weitzman, “The top five ways AI is transforming business,” Forbes, Nov. 21, 2022. [Online]. Available: https://www.forbes.com/sites/forbesbusinesscouncil/2022/11/21/the-top-five-ways-ai-is-transforming-business/?sh=1990ebd18e7f
Y. Hamdar, K. N. Massally, and Peiris, “Are countries ready for AI? How they can ensure ethical and responsible adoption | United Nations Development Programme,” UNDP, Apr. 25, 2023. https://www.undp.org/blog/are-countries-ready-ai-how-they-can-ensure-ethical-and-responsible-adoption (accessed Oct. 20, 2023).
E. Brynjolfsson, “The business of artificial intelligence,” Harvard Business Review, Nov. 08, 2022. https://hbr.org/2017/07/the-business-of-artificial-intelligence
R. L. Stevenson, Strange case of Dr. Jekyll and Mr. Hyde. 1886.
E. Ntoutsi et al., “Bias in data?driven artificial intelligence systems—An introductory survey,” Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, vol. 10, no. 3, Feb. 2020, doi: 10.1002/widm.1356.
K. Lum and W. Isaac, “To predict and serve?,” Significance, vol. 13, no. 5, pp. 14–19, Oct. 2016, doi: 10.1111/j.1740-9713.2016.00960.x.
A. Köchling and M. C. Wehner, “Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development,” Business Research, vol. 13, no. 3, pp. 795–848, Nov. 2020, doi: 10.1007/s40685-020-00134-w.
A. Klein, “Reducing bias in AI-based financial services | Brookings,” Brookings, Jul. 10, 2020. https://www.brookings.edu/articles/reducing-bias-in-ai-based-financial-services/ (accessed Oct. 20, 2023).
S. Barocas, M. Hardt, and A. Narayanan, Fairness and machine learning: Limitations and Opportunities. MIT Press, 2023.
“CODED BIAS — 7th Empire Media,” 7th Empire Media. https://www.7thempiremedia.com/films-codedbias
C. Kerry, “Protecting privacy in an AI-driven world | Brookings,” Brookings, Feb. 10, 2020. https://www.brookings.edu/articles/protecting-privacy-in-an-ai-driven-world/ (accessed Oct. 20, 2023).
H. Davidson, “China’s coronavirus health code apps raise concerns over privacy,” The Guardian, Jul. 01, 2020. [Online]. Available: https://www.theguardian.com/world/2020/apr/01/chinas-coronavirus-health-code-apps-raise-concerns-over-privacy
J. W. Burton and S. R. Soare, “Understanding the Strategic Implications of the Weaponization of Artificial Intelligence,” IEEE Xplore, Jul. 2019, doi: 10.23919/cycon.2019.8756866.
B. Dresp-Langley, “The weaponization of artificial intelligence: What the public needs to be aware of,” Frontiers in Artificial Intelligence, vol. 6, Mar. 2023, doi: 10.3389/frai.2023.1154184.
D. Harwell, “Faked Pelosi videos, slowed to make her appear drunk, spread across social media,” Washington Post, May 24, 2019. [Online]. Available: https://www.washingtonpost.com/technology/2019/05/23/faked-pelosi-videos-slowed-make-her-appear-drunk-spread-across-social-media/
M. Sadiq, “Real v fake: debunking the ‘drunk’ Nancy Pelosi footage - video,” The Guardian, Feb. 26, 2020. https://www.theguardian.com/us-news/video/2019/may/24/real-v-fake-debunking-the-drunk-nancy-pelosi-footage-video
G. Shao, “What ‘deepfakes’ are and how they may be dangerous,” CNBC, Jan. 17, 2020. [Online]. Available: https://www.cnbc.com/2019/10/14/what-is-deepfake-and-how-it-might-be-dangerous.html
B. Cheatham, K. Javanmardian, and H. Samandari, “Confronting the risks of artificial intelligence,” McKinsey & Company, Apr. 2019, [Online]. Available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/confronting-the-risks-of-artificial-intelligence
S. Russell, D. Dewey, and M. Tegmark, “Research priorities for robust and beneficial artificial intelligence,” Ai Magazine, vol. 36, no. 4, pp. 105–114, Dec. 2015, doi: 10.1609/aimag.v36i4.2577.
Mila, “Missing links in AI governance,” UNESCO, Aug. 2023, [Online]. Available: https://www.unesco.org/en/articles/missing-links-ai-governance
Future of Life Institute, “Asilomar AI Principles - Future of Life Institute,” Future of Life Institute, Oct. 19, 2020. https://futureoflife.org/person/asilomar-ai-principles/
M. Cheng, “What AI mavens can learn from a 1975 genetics conference,” Quartz, May 03, 2023. [Online]. Available: https://qz.com/what-ai-mavens-can-learn-from-a-1975-genetics-conferenc-1850305170
F. Heylighen, “A cognitive-systemic reconstruction of maslow’s theory of self-actualization,” Systems Research and Behavioral Science, vol. 37, no. 1, pp. 39–58, Jan. 1992, doi: 10.1002/bs.3830370105.
A. H. Maslow, A theory of human motivation. Simon and Schuster, 2013.
A. Turing, “I.—COMPUTING MACHINERY AND INTELLIGENCE,” Mind, vol. LIX, no. 236, pp. 433–460, Oct. 1950, doi: 10.1093/mind/lix.236.433.
B. J. Copeland and D. Proudfoot, “The computer, artificial intelligence, and the Turing Test,” in Springer eBooks, 2004, pp. 317–351. doi: 10.1007/978-3-662-05642-4_13.
S. Harnad, “The Turing Test is not a trick,” SIGART Newsletter, vol. 3, no. 4, pp. 9–10, Oct. 1992, doi: 10.1145/141420.141422.
M. E. Koltko-Rivera, “Rediscovering the later version of Maslow’s hierarchy of needs: Self-Transcendence and opportunities for theory, research, and unification,” Review of General Psychology, vol. 10, no. 4, pp. 302–317, Dec. 2006, doi: 10.1037/1089-2618.104.22.1682.
A. H. Maslow, Motivation and personality. New York?: Harper & Row, 1970.
R. Kurzweil, The singularity is near: When Humans Transcend Biology. Penguin, 2005.
V. Vinge, “The coming technological singularity,” 1993. https://philpapers.org/rec/VINTCT
Transcendence, (Apr. 18, 2014).
J. McCarthy, M. Minsky, N. Rochester, and C. E. Shannon, “A proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955,” Ai Magazine, vol. 27, no. 4, p. 12, Dec. 2006, doi: 10.1609/aimag.v27i4.1904.
Future of Life Institute, “AI Principles - Future of Life Institute,” Future of Life Institute, Jun. 08, 2023. https://futureoflife.org/open-letter/ai-principles/
H. Stapf-Fine, U. Bartosch, S. Bauberger, and A. Sülzen, “Policy Paper on the Asilomar Principles on Artificial Intelligence,” ResearchGate, Dec. 2018, [Online]. Available: https://www.researchgate.net/publication/329963051_Policy_Paper_on_the_Asilomar_Principles_on_Artificial_Intelligence
S. Sarangi and P. Sharma, Artificial intelligence. 2018. doi: 10.4324/9780429461002.
H. Snyder, “Literature review as a research methodology: An overview and guidelines,” Journal of Business Research, vol. 104, pp. 333–339, Nov. 2019, doi: 10.1016/j.jbusres.2019.07.039.
F. Jiang et al., “Artificial intelligence in healthcare: past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4, pp. 230–243, Jun. 2017, doi: 10.1136/svn-2017-000101.
S. E. Dilsizian and E. L. Siegel, “Artificial Intelligence in medicine and cardiac imaging: Harnessing big data and advanced computing to provide personalized medical diagnosis and treatment,” Current Cardiology Reports, vol. 16, no. 1, Dec. 2013, doi: 10.1007/s11886-013-0441-8.
H. Kim, E. Y. Kim, I. Lee, B. Bae, M. Park, and H. Nam, “Artificial Intelligence in Drug Discovery: A Comprehensive review of data-driven and machine learning approaches,” Biotechnology and Bioprocess Engineering, vol. 25, no. 6, pp. 895–930, Dec. 2020, doi: 10.1007/s12257-020-0049-y.
J. Vamathevan et al., “Applications of machine learning in drug discovery and development,” Nature Reviews Drug Discovery, vol. 18, no. 6, pp. 463–477, Apr. 2019, doi: 10.1038/s41573-019-0024-5.
D. Nahavandi, R. Alizadehsani, A. Khosravi, and U. R. Acharya, “Application of artificial intelligence in wearable devices: Opportunities and challenges,” Computer Methods and Programs in Biomedicine, vol. 213, p. 106541, Jan. 2022, doi: 10.1016/j.cmpb.2021.106541.
E. B. Boukherouaa et al., Powering the Digital Economy: Opportunities and risks of Artificial intelligence in finance. International Monetary Fund, 2021.
T. Mizio?ek, “Employing artificial intelligence in investment management,” in Routledge eBooks, 2021, pp. 161–174. doi: 10.4324/9781003095354-9.
S. Kiderlin, “Banking in the metaverse? A.I. could be about to change the way you manage your money,” CNBC, May 02, 2023. [Online]. Available: https://www.cnbc.com/2023/05/02/banking-in-the-metaverse-ai-could-change-money-management.html
L. Wewege and M. C. Thomsett, The digital banking revolution. 2019. doi: 10.1515/9781547401598.
D. J. Fagnant and K. M. Kockelman, “Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations,” Transportation Research Part A: Policy and Practice, vol. 77, pp. 167–181, Jul. 2015, doi: 10.1016/j.tra.2015.04.003.
B. Schoettle, “A survey of public opinion about autonomous and self-driving vehicles in the U.S., the U.K., and Australia,” Jul. 01, 2014. https://deepblue.lib.umich.edu/handle/2027.42/108384
R. L. Abduljabbar, H. Dia, S. Liyanage, and S. A. Bagloee, “Applications of Artificial Intelligence in Transport: An Overview,” Sustainability, vol. 11, no. 1, p. 189, Jan. 2019, doi: 10.3390/su11010189.
V. D. V. T. & J. Nick, “Artificial Intelligence in Education: Can AI bring the full potential of personalized learning to education?,” ideas.repec.org, 2019, [Online]. Available: https://ideas.repec.org/p/zbw/itse19/205222.html
E. Brynjolfsson and A. McAfee, The second machine age: work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company, 2014.
J. Bughin et al., “Skill shift: Automation and the future of the workforce,” McKinsey & Company, May 23, 2018. https://www.mckinsey.com/featured-insights/future-of-work/skill-shift-automation-and-the-future-of-the-workforce (accessed Oct. 20, 2023).
M. R. Frank et al., “Toward understanding the impact of artificial intelligence on labor,” Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 14, pp. 6531–6539, Mar. 2019, doi: 10.1073/pnas.1900949116.
J. Manyika, K. Sneader, and McKinsey Global Institute, “AI, automation, and the future of work: Ten things to solve for,” McKinsey & Company, Jun. 01, 2018. https://www.mckinsey.com/featured-insights/future-of-work/ai-automation-and-the-future-of-work-ten-things-to-solve-for (accessed Oct. 20, 2023).
S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, “Reshaping business with artificial intelligence: Closing the gap between ambition and action - ProQuest,” MIT Sloan Management Review, no. Vol. 59, Iss. 1, 2017.
K. Alicke et al., “Succeeding in the AI supply-chain revolution,” McKinsey & Company, Apr. 30, 2021. https://www.mckinsey.com/industries/metals-and-mining/our-insights/succeeding-in-the-ai-supply-chain-revolution (accessed Oct. 20, 2023).
K. Butner and IBM Institute for Business Value, “AI is reshaping the supply chain,” IBM, Jun. 01, 2017. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/cognitivesupplychain (accessed Oct. 20, 2023).
S. Mullainathan and J. Spiess, “Machine Learning: an Applied Econometric approach,” Journal of Economic Perspectives, vol. 31, no. 2, pp. 87–106, May 2017, doi: 10.1257/jep.31.2.87.
European Parliament, “Artificial intelligence: threats and opportunities | News | European Parliament,” Jun. 20, 2023. https://www.europarl.europa.eu/news/en/headlines/society/20200918STO87404/artificial-intelligence-threats-and-opportunities (accessed Oct. 20, 2023).
C. Bibby, J. Gordon, G. Schuler, and E. Stein, “The big reset: Data-driven marketing in the next normal,” McKinsey & Company, Mar. 2021, [Online]. Available: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-big-reset-data-driven-marketing-in-the-next-normal
G. Allen and T. Chan, “Artificial Intelligence and National Security | Belfer Center for Science and International Affairs,” Belfer Center for Science and International Affairs, Jul. 2017. https://www.belfercenter.org/publication/artificial-intelligence-and-national-security (accessed Oct. 20, 2023).
V. M. Hudson, “Introduction,” in Artificial Intelligence And International Politics, 1st ed., Routledge, pp. 1–8. doi: 10.4324/9780429033575-1.
A. Echterhölter, J. Schröter, and A. Sudmann, “How is Artificial Intelligence Changing Science? Research in the Era of Learning Algorithms.,” MediArXiv, pp. 1–24, Jun. 2021, doi: 10.33767/osf.io/28pnx.
C. B. Frey and M. A. Osborne, “The future of employment: How susceptible are jobs to computerisation?,” Technological Forecasting and Social Change, vol. 114, pp. 254–280, Jan. 2017, doi: 10.1016/j.techfore.2016.08.019.
Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, “Dissecting racial bias in an algorithm used to manage the health of populations,” Science, vol. 366, no. 6464, pp. 447–453, Oct. 2019, doi: 10.1126/science.aax2342.
S. Barocas and A. D. Selbst, “Big data’s disparate impact,” California Law Review, vol. 104, no. 3, p. 671, Jan. 2016, doi: 10.15779/z38bg31.
C. C. Miller, “When Algorithms Discriminate,” The New York Times, Jul. 09, 2015. [Online]. Available: https://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html
J. Buolamwini, “Gender Shades: Intersectional accuracy Disparities in commercial gender classification,” PMLR, Jan. 21, 2018. https://proceedings.mlr.press/v81/buolamwini18a.html
B. Cowgill, “The Impact of Algorithms on Judicial Discretion?: Evidence from Regression Discontinuities,” 2018. https://www.semanticscholar.org/paper/The-Impact-of-Algorithms-on-Judicial-Discretion-%3A-Cowgill/cdd4484708af448831eeb3d76a7c2d1e5b0a4ff2
A. Lambrecht and C. Tucker, “Algorithmic Bias? An Empirical Study into Apparent Gender-Based Discrimination in the Display of STEM Career Ads,” Social Science Research Network, Jan. 2016, doi: 10.2139/ssrn.2852260.
R. P. Bartlett, “Consumer-Lending discrimination in the era of FinTech *,” 2018. https://www.semanticscholar.org/paper/Consumer-Lending-Discrimination-in-the-Era-of-*-Bartlett/c9c4fcd3dbae4a33f7f985a184d9340be3b8db59
A. Datta, M. C. Tschantz, and A. Datta, “Automated Experiments on AD Privacy Settings: A tale of opacity, choice, and discrimination,” arXiv (Cornell University), Aug. 2014, doi: 10.48550/arxiv.1408.6491.
L. Sweeney, “Discrimination in online ad delivery,” Social Science Research Network, Jan. 2013, doi: 10.2139/ssrn.2208240.
S. U. Noble, Algorithms of oppression: How Search Engines Reinforce Racism. NYU Press, 2018.
J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine Bias,” ProPublica, May 23, 2016. Accessed: Oct. 21, 2023. [Online]. Available: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
J. Dressel and H. Farid, “The accuracy, fairness, and limits of predicting recidivism,” Science Advances, vol. 4, no. 1, Jan. 2018, doi: 10.1126/sciadv.aao5580.
J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms,” Big Data & Society, vol. 3, no. 1, p. 205395171562251, Jan. 2016, doi: 10.1177/2053951715622512.
F. A. Pasquale, The Black Box Society. 2015. doi: 10.4159/harvard.9780674736061.
C. S. Nam, J.-Y. Jung, and S. Lee, Human-Centered Artificial Intelligence: Research and Applications. Elsevier, 2022.
R. Calo, “Artificial Intelligence Policy: a roadmap,” Social Science Research Network, Jan. 2017, doi: 10.2139/ssrn.3015350.
M. Scherer, “Regulating Artificial intelligence Systems: Risks, challenges, competencies, and strategies,” Social Science Research Network, Jan. 2015, doi: 10.2139/ssrn.2609777.
N. B. Jones, “Computer science: The learning machines,” Nature, vol. 505, no. 7482, pp. 146–148, Jan. 2014, doi: 10.1038/505146a.
F. Bu, N. Wang, B. Jiang, and H. Liang, “‘Privacy by Design’ implementation: Information system engineers’ perspective,” International Journal of Information Management, vol. 53, p. 102124, Aug. 2020, doi: 10.1016/j.ijinfomgt.2020.102124.
A. Cavoukian, “Staying one step ahead of the GDPR: Embed privacy and security by design,” ideas.repec.org, 2018, [Online]. Available: https://ideas.repec.org/a/aza/csj000/y2018v2i2p173-180.html
J. P. Carlin and G. M. Graff, Dawn of the Code War: America’s Battle Against Russia, China, and the Rising Global Cyber Threat. PublicAffairs, 2018.
P. Scharre, Army of None: Autonomous weapons and the future of war. W. W. Norton & Company, 2018.
N. Bostrom, Superintelligence: Paths, Dangers, Strategies. Oxford University Press, USA, 2014.
M. Chui, J. Manyika, and M. Miremadi, “Where machines could replace humans—and where they can’t (yet),” McKinsey & Company, Jul. 2016, [Online]. Available: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet
K.-F. Lee, AI superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin, 2018.
K. D. Acemoglu and P. Restrepo, “Automation and New Tasks: How technology displaces and reinstates labor,” Journal of Economic Perspectives, vol. 33, no. 2, pp. 3–30, May 2019, doi: 10.1257/jep.33.2.3.
V. Eubanks, Automating inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, 2018.
C. O’Neil, Weapons of math destruction: How Big Data Increases Inequality and Threatens Democracy. Penguin UK, 2016.
S. Zuboff, The age of surveillance capitalism: The Fight for a Human Future at the New Frontier of Power. Profile Books, 2019.
S. Zheng et al., “The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies,” arXiv (Cornell University), Apr. 2020, [Online]. Available: https://arxiv.org/pdf/2004.13332.pdf
K. Yeung, “‘Hypernudge’: Big Data as a mode of regulation by design,” Information, Communication & Society, vol. 20, no. 1, pp. 118–136, May 2016, doi: 10.1080/1369118x.2016.1186713.
B. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, “The ethics of algorithms: Mapping the debate,” Big Data & Society, vol. 3, no. 2, p. 205395171667967, Dec. 2016, doi: 10.1177/2053951716679679.
B. Mittelstadt and L. Floridi, “The Ethics of Big Data: Current and foreseeable issues in biomedical contexts,” Science and Engineering Ethics, vol. 22, no. 2, pp. 303–341, May 2015, doi: 10.1007/s11948-015-9652-2.
C. Sandvig, “Auditing Algorithms?: Research Methods for Detecting Discrimination on Internet Platforms,” 2014. https://www.semanticscholar.org/paper/Auditing-Algorithms-%3A-Research-Methods-for-on-Sandvig-Hamilton/b7227cbd34766655dea10d0437ab10df3a127396
B. Cowgill, “Bias and productivity in humans and machines,” Social Science Research Network, Jan. 2019, doi: 10.2139/ssrn.3584916.
C. L. P. Chen and C. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Information Sciences, vol. 275, pp. 314–347, Aug. 2014, doi: 10.1016/j.ins.2014.01.015.
A. H. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” International Journal of Information Management, vol. 35, no. 2, pp. 137–144, Apr. 2015, doi: 10.1016/j.ijinfomgt.2014.10.007.
K. Crawford, “Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms,” Oct. 01, 2013. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2325784
S. Hajian and J. Domingo-Ferrer, “A Methodology for direct and indirect discrimination Prevention in Data Mining,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 7, pp. 1445–1459, Jul. 2013, doi: 10.1109/tkde.2012.72.
J. A. Kroll, “Accountable algorithms,” Penn Carey Law: Legal Scholarship Repository. https://scholarship.law.upenn.edu/penn_law_review/vol165/iss3/3
B. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, “The ethics of algorithms: Mapping the debate,” Big Data & Society, vol. 3, no. 2, p. 205395171667967, Dec. 2016, doi: 10.1177/2053951716679679.
O. Osoba, “Keeping artificial intelligence accountable to humans,” RAND, Aug. 2018, [Online]. Available: https://www.rand.org/blog/2018/08/keeping-artificial-intelligence-accountable-to-humans.html
M. Brundage et al., “The Malicious Use of Artificial intelligence: Forecasting, Prevention, and Mitigation,” arXiv (Cornell University), Feb. 2018, doi: 10.17863/cam.22520.
M. Ziewitz, “Governing algorithms,” Science, Technology, & Human Values, vol. 41, no. 1, pp. 3–16, Sep. 2015, doi: 10.1177/0162243915608948.
A. Chouldechova, “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments,” Big Data, vol. 5, no. 2, pp. 153–163, Jun. 2017, doi: 10.1089/big.2016.0047.
A. Jobin and M. Ienca, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence, vol. 1, no. 9, pp. 389–399, Sep. 2019, doi: 10.1038/s42256-019-0088-2.
L. Floridi et al., “AI4People—An Ethical Framework for a Good AI Society: Opportunities, risks, principles, and recommendations,” Minds and Machines, vol. 28, no. 4, pp. 689–707, Nov. 2018, doi: 10.1007/s11023-018-9482-5.
M. Tegmark, Life 3.0: Being human in the age of Artificial Intelligence. 2017. [Online]. Available: https://dl.acm.org/citation.cfm?id=3169322
L. Floridi, The Fourth Revolution: How the infosphere is reshaping human reality. 2014. [Online]. Available: http://ci.nii.ac.jp/ncid/BB16630552
L. Floridi and J. W. Sanders, “On the Morality of Artificial Agents,” Minds and Machines, vol. 14, no. 3, pp. 349–379, Aug. 2004, doi: 10.1023/b:mind.0000035461.63578.9d.
J. Powles and H. Hodson, “Google DeepMind and healthcare in an age of algorithms,” Health and Technology, vol. 7, no. 4, pp. 351–367, Mar. 2017, doi: 10.1007/s12553-017-0179-1.
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