Abstract
As artificial intelligence (AI) continues to proliferate into every area of modern life, there is no doubt that society has to think deeply about the potential impact, whether negative or positive, that it will have. Whilst scholars recognise that AI can usher in a new era of personal, social and economic prosperity, they also warn of the potential for it to be misused towards the detriment of society. Deliberate strategies are therefore required to ensure that AI can be safely integrated into society in a manner that would maximise the good for as many people as possible, whilst minimising the bad. One of the most urgent societal expectations of artificial agents is the need for them to behave in a manner that is morally relevant, i.e. to become artificial moral agents (AMAs). In this article, I will argue that exemplarism, an ethical theory based on virtue ethics, can be employed in the building of computationally rational AMAs with weak machine ethics. I further argue that three features of exemplarism, namely grounding in moral exemplars, meeting community expectations and practical simplicity, are crucial to its uniqueness and suitability for application in building AMAs that fit the ethos of AI4SG.
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The three main ethical theories in normative ethics are consequentialism, deontology and virtue ethics.
Often translated from Greek to English as “happiness”, “flourishing”, “well-being” or even the “good life”.
From a design perspective, this would mean using a learning-based approach to form an internal representation of moral values and a completely different decision-making procedure to make moral decisions. This is similar in approach to how Howard and Muntean (2016) designed their AMA, although they have slightly different reasons for using this technique.
Aristotle believed that a person needs a balance between the vices of deficiency and excess to be virtuous. This balance can be thought of as a conceptual mid-point between two opposite vices—a “golden mean”.
This scenario is mostly based on a collection of case studies on classroom ethics by Levinson and Fay (2016). I have merely replaced the human teacher with Robo-teacher in the scenario, and used different names for the student(s).
References
Abel, D., MacGlashan, J., & Littman, M.L. (2016). Reinforcement learning as a framework for ethical decision making. In AAAI workshop - technical report. www.aaai.org, (Vol. WS-16-01 pp. 54–61).
Abney, K. (2012). Robotics, ethical theory, and metaethics: a guide for the perplexed. In Lin, P, Abney, K, & Bekey, G (Eds.) Robot Ethics, the ethical and social implications of robotics, (Vol. 3 pp. 35–52): The MIT Press, chap.
Aghion, P.J., Benjamin, F., & Jones, C.I. (2017). Artificial intelligence and economic growth. Tech. rep., National Bureau of Economic Research.
Allen, C., & Wallach, W. (2011). Moral machines: contradiction in terms, or abdication of human responsibility?. In Lin, P., Abney, K., & Bekey, G.A. (Eds.) Robot ethics (p. 4): The MIT Press, chap.
Alzahrani, H. (2016). Artificial intelligence: uses and misuses. Global Journal of Computer Science and Technology 16(1s).
Anderson, M., & Anderson, S.L. (2007). Machine ethics: creating an ethical intelligent agent. AI Magazine, 28(4), 15. https://doi.org/10.1609/aimag.v28i4.2065http://www.aaai.org/ojs/index.php/aimagazine/article/view/2065.
Anderson, S.L., & Anderson, M. (2011). A prima facie duty approach to machine ethics and its application to elder care. In Workshops at the twenty-Fffth AAAI conference on artificial intelligence.
Annas, J. (2011). Intelligent virtue. Oxford: Oxford University Press.
Argall, B.D., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and autonomous systems, 57 (5), 469–483.
Baum, S.D. (2017). Social choice ethics in artificial intelligence. AI and Society (October) 1–12, https://doi.org/10.1007/s00146-017-0760-1.
Brys, T., Harutyunyan, A., Suay, H.B., Chernova, S., Taylor, M.E., & Nowé, A. (2015). Reinforcement learning from demonstration through shaping. In Twenty-fourth international joint conference on artificial intelligence.
Churchland, P.S. (2014). The neurobiological platform for moral values. Behaviour, 151(2-3), 283–296.
Cloos, C. (2005). The utilibot project: an autonomous mobile robot based on utilitarianism. In Machine ethics: papers from the 2005 AAAI fall symposium. http://philpapers.org/archive/CLOTUP.2.pdf (pp. 38–45).
Conitzer, V., Sinnott-Armstrong, W., Borg, J.S., Deng, Y., & Kramer, M. (2017). Moral decision making frameworks for artificial intelligence. In ISAIM. www.aaai.org (pp. 4831–4835).
Cowls, J., & Floridi, L. (2018). Prolegomena to a white paper on an ethical framework for a good AI society. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3198732.https://ssrn.com/abstract=3198732.
Dameski, A. (2018). A comprehensive ethical framework for AI entities : foundations. In Iklé, M., Franz, A., Rzepka, R., & Goertzel, B. (Eds.) International conference on artificial general intelligence, July. https://doi.org/10.1007/978-3-319-97676-1 (pp. 42–51): Springer.
Dignum, V. (2017). Responsible autonomy. In Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI-17). https://doi.org/10.24963/ijcai.2017/655, arXiv:1706.02513.
Duan, Yan., Andrychowicz, Marcin., Stadie, Bradly., Jonathan, Ho., Open, AI., Schneider, Jonas., Sutskever, Ilya., Abbeel, Pieter., & Zaremba, Wojciech. (2017). One-Shot Imitation Learning. In I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.) Advances in Neural Information Processing Systems 30. http://papers.nips.cc/paper/6709-one-shot-imitation-learning.pdf (pp. 1087–1098): Curran Associates, Inc.
Floridi, L., & Sanders, J.W. (2004). On the morality of artificial agents. Minds and machines, 14(3), 349–379. https://doi.org/10.2139/ssrn.1124296.
Gershman, S.J., Horvitz, E.J., & Tenenbaum, J.B. (2015). Computational rationality: a converging paradigm for intelligence in brains, minds, and machines. Science, 349(6245), 273–278. https://doi.org/10.1126/science.aac6076. www.sciencemag.orgpapers2://publication/uuid/20A0106C-9CBA-472D-AAFB-69231964766F, arXiv:1011.1669v3.
Gips, J. (1995). Towards the ethical robot. In Android epistemology (pp. 243–252): MIT Press.
Himma, K.E. (2008). Artificial agency, consciousness, and the criteria for moral agency: what properties must an artificial agent have to be a moral agent? Ethics and Information Technology, 11(1), 19–29. https://doi.org/10.1007/s10676-008-9167-5.
Horvitz, E.J. (1987). Reasoning about beliefs and actions under computational resource constraints. In Proceedings of the third workshop on uncertainty in artificial intelligence, AAAI and Association for Uncertainty in Artificial Intelligence, July. http://erichorvitz.com/u87.htm (pp. 429–444).
Howard, D., & Muntean, I. (2016). A minimalist model of the artificial autonomous moral agent (AAMA). In AAAI Spring Symposium Series.
Hursthouse, R., & Pettigrove, G. (2018). Virtue ethics. In Zalta, E N (Ed.) The stanford encyclopedia of philosophy, winter 2018 edn, Metaphysics Research Lab, Stanford University.
Johnson, D.G. (2006). Computer systems: moral entities but not moral agents. Machine Ethics, 9780521112, 168–183. https://doi.org/10.1017/CBO9780511978036.012.
Kiela, D. (2017). Deep embodiment: grounding semantics in perceptual modalities. Tech. rep., University of Cambridge, Computer Laboratory. http://www.cl.cam.ac.uk/.
Kuipers, B. (2016). Human-like morality and ethics for robots.
van Lent, M., & Laird, J.E. (2001). Learning procedural knowledge through observation. In K-CAP (pp. 179–186), DOI https://doi.org/10.1145/500737.500765, (to appear in print).
Levinson, M., & Fay, J. (2016). Dilemmas of educational ethics: cases and commentaries. Cambridge: Harvard Education Press.
Liao, S.M. (2010). The basis of human moral status. Journal of Moral Philosophy, 7(2), 1–31. https://doi.org/10.1163/174552409X12567397529106.
Mayo, M.J. (2003). Symbol grounding and its implications for artificial intelligence. In Proceedings of the 26th Australasian computer science conference-Volume 16. http://portal.acm.org/citation.cfm?id=783106.783113&type=series, (Vol. 16 pp. 55–60). Darlinghurst: Australian Computer Society, Inc.
Miller, F.D. (1984). Aristotle on rationality in action. The Review of Metaphysics, 37(3), 499–520. https://www.jstor.org/stable/20128047.
Moor, J.H. (2006). The nature, importance, and difficulty of machine ethics. IEEE intelligent systems, 21(4), 18–21.
Parthemore, J., & Whitby, B. (2013). What makes any agent a moral agent? Reflections on machine consciousness and moral agency. International Journal of Machine Consciousness, 5(2), 105–129. https://pdfs.semanticscholar.org/3ff2/49fe3c8b3a2c94ae762b76b2dd0203f1f789.pdf.
Parthemore, J., & Whitby, B. (2014). Moral agency, moral responsibility, and artifacts: what existing artifacts fail to achieve (and why), and why they, nevertheless, can (and do!) make moral claims upon us . International Journal of Machine Consciousness, 6 (2), 141–161. https://doi.org/10.1142/S1793843014400162, http://www.worldscientific.com/doi/abs/10.1142/S1793843014400162.
Peterson, M. (2009). An introduction to decision theory. In Cambridge introductions to philosophy. Cambridge: Cambridge University Press, DOI https://doi.org/10.1017/CBO9780511800917, (to appear in print).
Pontier, M., & Hoorn, J. (2012). Toward machines that behave ethically better than humans do. In Proceedings of the annual meeting of the cognitive science society, Vol. 34.
Prasad, M. (2018). Social choice and the value alignment problem. In Artificial intelligence safety and security (pp. 291–314). London: Chapman and Hall/CRC.
Rottschaefer, W.A. (2000). Naturalizing ethics: the biology and psychology of moral agency. Zygon®;, 35(2), 253–286.
Russell, S.J., & Norvig, P. (2009). Artifical intelligence: a modern approach, 3rd edn. Upper Saddle River: Prentice Hall. https://doi.org/10.1017/S0269888900007724. arXiv:1707.02286, arXiv:1011.1669v3.
Scheutz, M., & Malle, B.F. (2017). Moral robots. In Johnson, L.S.M., & Rommelfanger, K.S. (Eds.) The Routledge handbook of neuroethics. https://doi.org/10.4324/9781315708652.ch24 (p. 24): Routledge, chap.
Simon, H.A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
Slote, M. (1995). Agent-based virtue ethics. Midwest Studies in Philosophy, 20 (1), 83–101.
Sullins, J.P. (2006). When is a robot a moral agent? IRIE: International Review of Information Ethics http://sonoma-dspace.calstate.edu/handle/10211.1/427.
Szutta, N. (2019). Exemplarist moral theory–some pros and cons. Journal of Moral Education, 48(3), 280–290. https://doi.org/10.1080/03057240.2019.1589435.
Torrance, S. (2008). Ethics and consciousness in artificial agents. AI and Society, 22(4), 495–521. https://doi.org/10.1007/s00146-007-0091-8.
Vamplew, P., Dazeley, R., Foale, C., Firmin, S., & Mummery, J. (2018). Human-aligned artificial intelligence is a multiobjective problem. Ethics and Information Technology, 20(1), 27–40. https://doi.org/10.1007/s10676-017-9440-6.
Wallach, W., Franklin, S., & Allen, C. (2010). A conceptual and computational model of moral decision making in human and artificial agents. Topics in Cognitive Science, 2(3), 454–485. https://doi.org/10.1111/j.1756-8765.2010.01095.x.
Zagzebski, L. (2010). Exemplarist virtue theory. Metaphilosophy, 41(1-2), 41–57. https://doi.org/10.1111/j.1467-9973.2009.01627.x.
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Mabaso, B.A. Artificial Moral Agents Within an Ethos of AI4SG. Philos. Technol. 34 (Suppl 1), 7–21 (2021). https://doi.org/10.1007/s13347-020-00400-z
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DOI: https://doi.org/10.1007/s13347-020-00400-z