Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis
Abstract
:1. Introduction
- -
- Maximizing societal welfare;
- -
- Avoiding harm to stakeholders;
- -
- Promoting transparency and accountability.
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- OECD. Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. 2021. Available online: https://www.oecd.org/finance/artificial-intelligence-machine-learningbig-data-in-finance.htm (accessed on 1 June 2023).
- Cao, L. AI in Finance: A Review. 10 July 2020. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3647625 (accessed on 1 June 2023).
- Cao, L.; Yang, Q.; Yu, P.S. Data science and AI in FinTech: An overview. Int. J. Data Sci. Anal. 2021, 12, 81–89. [Google Scholar] [CrossRef]
- Hamayel, M.J.; Owda, A.Y. A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms. AI 2021, 2, 477–496. [Google Scholar] [CrossRef]
- Kumar, R.; Singh, D.; Srinivasan, K.; Hu, Y. AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions. Healthcare 2023, 11, 81. [Google Scholar] [CrossRef] [PubMed]
- Miura, R.; Pichl, L.; Kaizoji, T. Artificial Neural Networks for Realized Volatility Prediction in Cryptocurrency Time Series. In Advances in Neural Networks—ISNN 2019; Lu, H., Tang, H., Wang, Z., Eds.; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2019; Volume 11554. [Google Scholar]
- Patel, M.M.; Tanwar, S.; Gupta, R.; Kumar, N. A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions. J. Inf. Secur. Appl. 2020, 55, 102583. [Google Scholar] [CrossRef]
- Sadman, N.; Ahsan, M.M.; Rahman, A.; Siddique, Z.; Gupta, K.D. Promise of AI in DeFi, a Systematic Review. Digital 2022, 2, 88–103. [Google Scholar] [CrossRef]
- Sebastião, H.; Godinho, P. Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financ. Innov. 2021, 7, 1–30. [Google Scholar] [CrossRef]
- Makarov, I.; Schoar, A. BIS Working Papers No 1061: Cryptocurrencies and Decentralized Finance. Bank for International Settlements. 2022. Available online: https://www.bis.org/publ/work1061.pdf (accessed on 1 June 2023).
- Narain, A.; Moretti, M. Regulating Crypto. International Monetary Fund. 2022. Available online: https://www.imf.org/en/Publications/fandd/issues/2022/09/Regulating-crypto-Narain-Moretti (accessed on 1 June 2023).
- Chordia, T.; Goyal, A.; Lehmann, B.; Saar, G. High-frequency trading. J. Financ. Mark. 2023, 16, 637–645. [Google Scholar] [CrossRef]
- Bin Sarhan, B.; Altwaijry, N. Insider Threat Detection Using Machine Learning Approach. Appl. Sci. 2023, 13, 259. [Google Scholar] [CrossRef]
- Boukherouaa, E.B.; Shabsigh, M.G.; AlAjmi, K.; Deodoro, J.; Farias, A.; Iskender, E.S.; Mirestean, A.T.; Ravikumar, R. Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance; International Monetary Fund: Washington, DC, USA, 2021; Volume 2021. [Google Scholar] [CrossRef]
- Comiter, M. Attacking Artificial Intelligence: AI’s Security Vulnerability and What Policymakers Can Do About It. 2019. Available online: https://www.belfercenter.org/publication/AttackingAI (accessed on 1 June 2023).
- Corbett-Davies, S.; Pierson, E.; Feller, A.; Goel, S.; Huq, A. Algorithmic Decision Making and the Cost of Fairness. In Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13–17 August 2017; pp. 797–806. [Google Scholar]
- Digalaki, E. The Impact of Artificial Intelligence in the Banking Sector & How AI is Being Used in 2021. Insider, 13 January 2021. Available online: https://www.businessinsider.com/ai-in-banking-report(accessed on 1 June 2023).
- European Central Bank (ECB). Bringing Artificial Intelligence to Banking Supervision. 2019. Available online: https://www.bankingsupervision.europa.eu/press/publications/newsletter/2019/html/ssm.nl191113_4.en.html (accessed on 1 June 2023).
- Fares, O.H.; Butt, I.; Lee, S.H.M. Utilization of artificial intelligence in the banking sector: A systematic literature review. J. Financ. Serv. Mark. 2022, 1–18. [Google Scholar] [CrossRef]
- Fuster, A.; Goldsmith-Pinkham, P.; Ramadorai, T.; Walther, A. Predictably Unequal? The Effects of Machine Learning on Credit Markets. Mimeo. 2020. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3072038 (accessed on 1 June 2023).
- Friedman, M. The social responsibility of business is to increase its profits. The New York Times Magazine, 13 September 1970; 33–37. [Google Scholar]
- Johnson, D.G.; Powers, T.M. Computer Systems and Responsibility: A Normative Look at Technological Complexity. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2008, 38, 733–743. [Google Scholar] [CrossRef]
- Fama, E.F. Efficient Capital Markets: A Review of Theory and Empirical Work. J. Financ. 1970, 25, 383–417. [Google Scholar] [CrossRef]
- Aliber, R.Z.; Kindleberger, C.P.; McCauley, R.N. Bitcoin: Worse than a Ponzi. In Manias, Panics, and Crashes; Palgrave Macmillan: London, UK, 2023. [Google Scholar] [CrossRef]
- Blackman, R. A Practical Guide to Building AI Ethics; Harvard Business Review: Boston, MA, USA, 2020. [Google Scholar]
- Blackman, R.; Ammanath, B. Ethics and AI: 3 Conversations Companies Need to Have; Harvard Business Review: Boston, MA, USA, 2022. [Google Scholar]
- Mill, J.S. Utilitarianism; Parker, Son, and Bourn: London, UK, 1863. [Google Scholar]
- Mill, J.S. Utilitarianism, London: Fraser; Collected in Collected Works of John Stuart Mill; Robson, J.M., Ed.; Routledge: London, UK, 1991; Volume 10, pp. 203–259. [Google Scholar]
- Alibašić, H. The Administrative and Ethical Considerations of Climate Resilience: The Politics and Consequences of Climate Change. Public Integr. 2020, 24, 33–50. [Google Scholar] [CrossRef]
- Alibašić, H. Strategic Resilience and Sustainability Planning: Management Strategies for Sustainable and Climate-Resilient Communities and Organizations; Springer: Berlin/Heidelberg, Germany, 2022. [Google Scholar]
- Alibašić, H. Hyper-engaged citizenry, negative governance and resilience: Impediments to sustainable energy projects in the United States. Energy Res. Soc. Sci. 2023, 100, 103072. [Google Scholar] [CrossRef]
- Bentham, J. An Introduction to the Principles of Morals and Legislation; Clarendon Press: Oxford, UK, 1789. [Google Scholar]
- Moore, G.E. Principia Ethica; Cambridge University Press: Cambridge, UK, 1903. [Google Scholar]
- Wood, A.W. How a Kantian Decides What to Do. In The Palgrave Kant Handbook; Matthew, C.A., Ed.; Palgrave Macmillan: London, UK, 2017; pp. 263–284. [Google Scholar] [CrossRef]
- Bryman, A. Social Research Methods, 5th ed.; Oxford University Press: New York, NY, USA, 2016. [Google Scholar]
- Shaw, W.H. Business Ethics; Cengage Learning: Boston, MA, USA, 2015. [Google Scholar]
- Rachels, J.; Rachels, S. The Elements of Moral Philosophy, 9th ed.; McGraw-Hill: New York, NY, USA, 2019. [Google Scholar]
- Shafer-Landau, R. The Fundamentals of Ethics; Oxford University Press: New York, NY, USA, 2018. [Google Scholar]
- Smart, J.J.C. An Outline of a System of Utilitarian Ethics; Smart, J.J.C., Williams, B., Eds.; Utilitarianism: For and Against; Cambridge University Press: Cambridge, UK, 1973; pp. 3–74. [Google Scholar]
- Williams, B. A Critique of Utilitarianism. In Utilitarianism: For and Against; Smart, J.J.C., Williams, B., Eds.; Cambridge University Press: Cambridge, UK, 1973; pp. 75–150. [Google Scholar]
- Bostrom, N. Superintelligence: Paths, Dangers, Strategies; Oxford University Press: New York, NY, USA, 2014. [Google Scholar]
- Foot, P. Utilitarianism and the Virtues. Mind 1985, 94, 196–209. [Google Scholar] [CrossRef] [Green Version]
- Goodin, R.E. Utilitarianism as a Public Philosophy; Cambridge University Press: Cambridge, UK, 1995. [Google Scholar]
- Baumann, M. Consequentializing and Underdetermination. Australas. J. Philos. 2019, 97, 511–527. [Google Scholar] [CrossRef]
- Portmore Douglas, W. Consequentializing. In The Stanford Encyclopedia of Philosophy; 2022 ed.; Edward, N.Z., Uri, N., Eds.; Stanford University: Stanford, CA, USA, 2022; Available online: https://plato.stanford.edu/archives/fall2022/entries/consequentializing/ (accessed on 1 June 2023).
- Portmore Douglas, W. Consequentializing Moral Theories. Pac. Philos. Q. 2007, 88, 39–73. [Google Scholar] [CrossRef]
- Sidgwick, H.M. Barratt on ‘The Suppression of Egoism’. Mind Orig. Ser. 1877, 2, 411–412. [Google Scholar] [CrossRef]
- Schroeder, S.A. Consequentializing and Its Consequences. Philos. Stud. 2017, 174, 1475–1497. [Google Scholar] [CrossRef]
- Scheffler, S. The Rejection of Consequentialism: A Philosophical Investigation of the Considerations Underlying Rival Moral Conceptions; Revised Edition; Clarendon Press: Oxford, UK, 1994. [Google Scholar]
- Tenenbaum, S. The Perils of Earnest Consequentializing. Philos. Phenomenol. Res. 2014, 88, 233–240. [Google Scholar] [CrossRef]
- Brown, C. Consequentialize This. Ethics 2011, 121, 749–771. [Google Scholar] [CrossRef] [Green Version]
- Dreier, J. In Defense of Consequentializing. In Oxford Studies in Normative Ethics; Timmons, M., Ed.; Oxford University Press: New York, NY, USA, 2011; Volume 1, pp. 97–119. [Google Scholar] [CrossRef]
- Hooker, B. Ideal Code, Real World: A Rule-Consequentialist Theory of Morality; Clarendon Press: Oxford, UK, 2000. [Google Scholar]
- Howard, N.R. Consequentialism and the Agent’s Point of View. Ethics 2022, 132, 787–816. [Google Scholar] [CrossRef]
- Lousie, J. Relativity of Value and the Consequentialist Umbrella. Philos. Q. 2004, 54, 518–536. [Google Scholar] [CrossRef]
- Berlin, I. Four Essays on Liberty; Oxford University Press: New York, NY, USA, 1969. [Google Scholar]
- Sandel, M.J. Justice: What’s the Right Thing to Do? Farrar, Straus and Giroux: New York, NY, USA, 2009. [Google Scholar]
- Carroll, A.B. The pyramid of corporate social responsibility: Toward the moral management of organizational stakeholders. Bus. Horiz. 1991, 34, 39–48. [Google Scholar] [CrossRef]
- Rawls, J. A Theory of Justice (Revised Edition); Harvard University Press: Cambridge, MA, USA, 2005. [Google Scholar]
- Singer, P. The Expanding Circle: Ethics, Evolution, and Moral Progress; Princeton University Press: Princeton, NJ, USA, 2011. [Google Scholar]
- Harsanyi, J.C. Morality and the Theory of Rational Behavior. Soc. Res. 1977, 44, 623–656. [Google Scholar] [CrossRef]
- Sandel Michael, J. What Money Can’t Buy: The Moral Limits of Markets. Farrar, Straus and Giroux. Tann. Lect. Hum. Values 2012, 21, 87–122. [Google Scholar]
- Tavani, H.T. Ethics and Technology: Controversies, Questions, and Strategies for Ethical Computing; John Wiley & Sons: Hoboken, NJ, USA, 2016. [Google Scholar]
- Floridi, L. The Ethics of Information; Oxford University Press: New York, NY, USA, 2013. [Google Scholar]
- Floridi, L. The Fourth Revolution: How the Infosphere is Reshaping Human Reality; Oxford University Press: Oxford, UK, 2014. [Google Scholar]
- Etzioni, A. The Common Good; Columbia University Press: New York, NY, USA, 2019. [Google Scholar]
- Yin, R.K. Case Study Research: Design and Methods; Sage Publications: New York, NY, USA, 2013. [Google Scholar]
- Allen, H.J.; Kharlf, O.; Yang, Y.; Miller, H. Why FTX Was an Empty Black Box All Along. Popular Media, 23 November 2022; 485. Available online: https://digitalcommons.wcl.american.edu/pub_disc_media/485(accessed on 10 June 2023).
- Beyoud, L.; Yang, Y.; Kharif, O. Sam Bankman-Fried’s FTX Empire Faces US Probe into Client Funds, Lending. Bloomberg, 9 November 2022. Available online: https://www.bloomberg.com/news/articles/2022-11-09/us-probes-ftx-empire-over-handling-of-client-funds-and-lending#xj4y7vzkg(accessed on 1 June 2023).
- Chohan, U.W. FTX, Sam Bankman-Fried, and Elite Capture. 4 February 2023. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4350992 (accessed on 1 June 2023).
- Chohan, U.W. FTX, Cryptocurrencies, and Anarchism Ignored. 5 February 2023. Available online: https://ssrn.com/abstract=4350999 (accessed on 1 June 2023).
- Fagan, F. The Collapse of FTX: Case, Materials, and Questions. 10 February 2023. Available online: https://ssrn.com/abstract=4353923 (accessed on 1 June 2023).
- Fu, S.; Wang, Q.; Yu, J.; Chen, S. FTX Collapse: A Ponzi Story. arXiv 2022, arXiv:2212.09436. [Google Scholar]
- Haldar, A. The Case That Foreshadowed the Lessons of the FTX Collapse. Wired, 21 December 2022. Available online: https://www.wired.com/story/cryptocurrency-sbf-ftx-microfinance/(accessed on 11 June 2023).
- O’Brien, K. The Deepening Predicament of Samuel Bankman-Fried. Reuters, 10 April 2023. Available online: https://www.reuters.com/legal/legalindustry/deepening-predicament-samuel-bankman-fried-2023-04-10/(accessed on 11 June 2023).
- Ramasubramanian, G. Corporate Governance Failures Due to Behavioral Factors. 16 February 2023. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4360876 (accessed on 11 June 2023).
- Roy, D.; Anand, P.; Chandra, S. FTX collapse: The chronicle and the implications. Vinimaya 2023, 43, 45–51. Available online: https://login.ezproxy.lib.uwf.edu/login?url=https://www.proquest.com/scholarly-journals/ftx-collapse-chronicle-implications/docview/2818285695/se-2 (accessed on 11 June 2023).
- Securities and Exchange Commission. Complaint (No. PR2022-219). 2022. Available online: https://www.sec.gov/litigation/complaints/2022/comp-pr2022-219.pdf (accessed on 11 June 2023).
- Securities and Exchange Commission. Press Release (No. 2022-219). 2022. Available online: https://www.sec.gov/news/press-release/2022-219 (accessed on 11 June 2023).
- Securities and Exchange Commission. Litigation Release (No. LR25616). 2023. Available online: https://www.sec.gov/litigation/litreleases/2023/lr25616.htm (accessed on 11 June 2023).
- Schickler, J. FTX Examiner Appointment Referred to Court of Appeals by District Judge. CoinDesk, 30 May 2023. Available online: https://www.coindesk.com/policy/2023/05/30/ftx-examiner-appointment-referred-to-court-of-appeals-by-district-judge/(accessed on 11 June 2023).
- Zahn, M. A Timeline of Cryptocurrency Exchange FTX’s Historic Collapse. Abc News, 13 December 2022. Available online: https://abcnews.go.com/Business/timeline-cryptocurrency-exchange-ftxs-historic-collapse/story?id=93337035(accessed on 11 June 2023).
- MacAskil, W. Doing Good Better: How Effective Altruism Can Help You Help Others, Do Work that Matters, and Make Smarter Choices about Giving Back; Reprint Edition; Penguin Publishing Group: New York, NY, USA, 2016. [Google Scholar]
- Brynjolfsson, E.; McAfee, A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies; WW Norton & Company: New York, NY, USA, 2014. [Google Scholar]
- Burrell, J. How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data Soc. 2016, 3, 2053951715622512. [Google Scholar] [CrossRef]
- Floridi, L.; Sanders, J.W. On the morality of artificial agents. Minds Mach. 2004, 14, 349–379. [Google Scholar] [CrossRef] [Green Version]
- Müller, V.C. Ethics of artificial intelligence and robotics. In Stanford Encyclopedia of Philosophy; Edward, N.Z., Ed.; Stanford University: Stanford, CA, USA, 2020; Available online: http://plato.stanford.edu/ (accessed on 10 June 2023).
- Baydin, A.G.; Pearlmutter, B.A.; Radul, A.A.; Siskind, J.M. Automatic Differentiation in Machine Learning: A Survey. arXiv 2015, arXiv:1502.05767. [Google Scholar]
- Caliskan, A.; Bryson, J.J.; Narayanan, A. Semantics derived automatically from language corpora contain human-like biases. Science 2017, 356, 183–186. [Google Scholar] [CrossRef] [Green Version]
- Hevelke, A.; Nida-Rümelin, J. Responsibility for crashes of autonomous vehicles: An ethical analysis. Sci. Eng. Ethics 2015, 21, 619–630. [Google Scholar] [CrossRef]
- Fortes, P.; Baquero, P.; Amariles, D. Artificial Intelligence Risks and Algorithmic Regulation. Eur. J. Risk Regul. 2022, 13, 357–372. [Google Scholar] [CrossRef]
- Russell, S.J.; Norvig, P. Artificial Intelligence: A Modern Approach; Pearson Education, Inc.: London, UK, 2020. [Google Scholar]
- Taddeo, M.; Floridi, L. How AI can be a force for good. Science 2018, 361, 751–752. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Brynjolfsson, E.; Mitchell, T. What can machine learning do? Workforce implications. Science 2017, 358, 1530–1534. [Google Scholar] [CrossRef]
- Stalnaker, R. Knowledge, Belief and Counterfactual Reasoning in Games; Economics and Philosophy; Cambridge University Press: Cambridge, UK, 1996; Volume 12, pp. 133–163. [Google Scholar]
- Huang, P.H. How To Teach Business Law Students About Emotional Intelligence, Resilience, and SBF. In How to Account for Trauma and Emotions in Legal Teaching; Lindsay, M.H., Mallik, K., Eds.; Forthcoming, U of Colorado Law Legal Studies Research Paper No. 23-1; Edward Elgar Publishing: Cheltenham, UK, 2023; Available online: https://ssrn.com/abstract=4317339 (accessed on 4 January 2023).
- Solowey, J.; Schulp, J. What Congress Should Do about Crypto Exchanges; Cato Institute: Washington, DC, USA, 2022; Available online: https://policycommons.net/artifacts/3344820/what-congress-should-do-about-crypto-exchanges/4143724/ (accessed on 17 June 2023).
- Unal, I.M.; Aysan, A.F. Fintech, Digitalization, and Blockchain in Islamic Finance: Retrospective Investigation. FinTech 2022, 1, 388–398. [Google Scholar] [CrossRef]
- Stalnaker, R.; Selinger, E.; Hartzog, W. Knowledge, Belief and Counterfactual Reasoning in Games. Econ. Philos. 1996, 12, 133–163. [Google Scholar] [CrossRef] [Green Version]
- Davoudi, A.; Wanigatunga, A.A.; Kheirkhahan, M.; Corbett, D.B.; Mendoza, T.; Battula, M.; Ranka, S.; Fillingim, R.B.; Manini, T.M.; Rashidi, P. Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study. JMIR Mhealth Uhealth 2019, 7, e11270. [Google Scholar] [CrossRef]
- Čartolovni, A.; Tomičić, A.; Lazić Mosler, E. Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review. Int. J. Med. Inform. 2022, 161, 104738. [Google Scholar] [CrossRef] [PubMed]
- O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy; Crown: New York, NY, USA, 2016. [Google Scholar]
- Rahwan, I.; Cebrian, M.; Obradovich, N.; Bongard, J.; Bonnefon, J.F.; Breazeal, C.; Crandall, J.W.; Christakis, N.A.; Couzin, I.D.; Jackson, M.O.; et al. Machine behaviour. Nature 2019, 568, 477–486. [Google Scholar] [CrossRef] [Green Version]
- Beauchamp, T.L.; Childress, J.F. Principles of Biomedical Ethics; Oxford University Press: New York, NY, USA, 2013. [Google Scholar]
- Weizenbaum, J. Computer Power and Human Reason: From Judgement to Calculation; W. H. Freeman & Co.: New York, NY, USA, 1976. [Google Scholar]
- Petersen, S. Ethics of robot servitude. J. Exp. Theor. Artif. Intell. 2007, 19, 43–54. [Google Scholar] [CrossRef]
Ethical Framework | Key Principles | Application in Cryptocurrency Trading | Example in Cryptocurrency Trading |
---|---|---|---|
Utilitarianism | Maximize overall happiness and minimize overall suffering. | Assess whether AI in cryptocurrency trading would create more happiness or suffering. | A cryptocurrency exchange uses AI to prevent market manipulation, resulting in more trust in the market and increased happiness among traders. |
Egoism | Maximize self-interest. | Assess whether AI in cryptocurrency trading would benefit the self-interest of the individual or group. | A cryptocurrency exchange uses AI to make more accurate trades, resulting in higher profits for the exchange and its investors. |
Hedonistic Egoism | Maximize pleasure and minimize pain for oneself. | Assess whether AI in cryptocurrency trading would create more pleasure or pain for oneself. | Cryptocurrency trader uses AI to make more profitable trades, resulting in more pleasure for themselves. |
Ethical Altruism | Maximize the overall well-being of others. | Assess whether AI in cryptocurrency trading would create more overall well-being for others. | A cryptocurrency exchange uses AI to prevent market manipulation, resulting in increased trust and overall well-being for traders. |
Rule Consequentialism | Follow rules that maximize overall happiness and minimize overall suffering. | Assess whether AI in cryptocurrency trading would follow rules that lead to more overall happiness and less overall suffering. | A cryptocurrency exchange uses AI to follow strict regulations, resulting in increased trust in the market and overall happiness for traders. |
Act Consequentialism | Make decisions that maximize overall happiness and minimize overall suffering in each situation. | Assess whether AI in cryptocurrency trading would make decisions that lead to more overall happiness and less overall suffering in each situation. | A cryptocurrency trader uses AI to make decisions about trades, resulting in more overall happiness and less overall suffering for traders. |
Ethical Outcomes | Potential Benefits | Potential Detriments | Alignment with Consequentialist Theories |
---|---|---|---|
Market Efficiency | AI and ML can enhance market efficiency by quickly processing large amounts of data and making accurate predictions. | AI and ML could contribute to market manipulation and financial instability if misused. | If the use of AI and ML leads to a more efficient and fair market, it will align with consequentialist theories, prioritizing the greatest good for the greatest number. |
Risk Management | AI and ML can improve risk management by identifying potential risks and generating real-time alerts. | Over-reliance on AI and ML could lead to complacency and a lack of human oversight, potentially exacerbating risks. | If the use of AI and ML effectively manages risks and prevents harm, it would align with consequentialist theories. |
Access to Financial Markets | AI and ML can democratize access to financial markets by providing sophisticated trading tools to the general public. | If not properly regulated, AI and ML could be used to exploit less knowledgeable investors, leading to unfair outcomes. | If the use of AI and ML broadens access to financial markets and promotes financial inclusion, it would align with consequentialist theories. |
Regulatory Compliance | AI and ML can help firms comply with regulatory requirements by automating compliance tasks. | If used unethically, AI and ML could be used to evade regulatory scrutiny and engage in illegal activities. | If the use of AI and ML promotes regulatory compliance and protects investors, it would align with consequentialist theories. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Alibašić, H. Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis. FinTech 2023, 2, 430-443. https://doi.org/10.3390/fintech2030024
Alibašić H. Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis. FinTech. 2023; 2(3):430-443. https://doi.org/10.3390/fintech2030024
Chicago/Turabian StyleAlibašić, Haris. 2023. "Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis" FinTech 2, no. 3: 430-443. https://doi.org/10.3390/fintech2030024
APA StyleAlibašić, H. (2023). Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis. FinTech, 2(3), 430-443. https://doi.org/10.3390/fintech2030024