Machine ethics
Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents.[1] Machine ethics differs from other ethical fields related to engineering and technology. It should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with technology's grander social effects.[2]
Definitions
James H. Moor, one of the pioneering theoreticians in the field of computer ethics, defines four kinds of ethical robots. As an extensive researcher on the studies of philosophy of artificial intelligence, philosophy of mind, philosophy of science, and logic, Moor defines machines as ethical impact agents, implicit ethical agents, explicit ethical agents, or full ethical agents. A machine can be more than one type of agent.[3]
- Ethical impact agents: These are machine systems that carry an ethical impact whether intended or not. At the same time, they have the potential to act unethically. Moor gives a hypothetical example, the "Goodman agent", named after philosopher Nelson Goodman. The Goodman agent compares dates but has the millennium bug. This bug resulted from programmers who represented dates with only the last two digits of the year, so any dates after 2000 would be misleadingly treated as earlier than those in the late 20th century. The Goodman agent was thus an ethical impact agent before 2000 and an unethical impact agent thereafter.
- Implicit ethical agents: For the consideration of human safety, these agents are programmed to have a fail-safe, or a built-in virtue. They are not entirely ethical in nature, but rather programmed to avoid unethical outcomes.
- Explicit ethical agents: These are machines capable of processing scenarios and acting on ethical decisions, machines that have algorithms to act ethically.
- Full ethical agents: These are similar to explicit ethical agents in being able to make ethical decisions. But they also have human metaphysical features (i.e., have free will, consciousness, and intentionality).
(See artificial systems and moral responsibility.)
History
Before the 21st century the ethics of machines had largely been the subject of
One thing that is apparent from the above discussion is that intelligent machines will embody values, assumptions, and purposes, whether their programmers consciously intend them to or not. Thus, as computers and robots become more and more intelligent, it becomes imperative that we think carefully and explicitly about what those built-in values are. Perhaps what we need is, in fact, a theory and practice of machine ethics, in the spirit of Asimov's three laws of robotics.[4]
In 2004, Towards Machine Ethics[5] was presented at the AAAI Workshop on Agent Organizations: Theory and Practice.[6] Theoretical foundations for machine ethics were laid out.
At the AAAI Fall 2005 Symposium on Machine Ethics, researchers met for the first time to consider implementation of an ethical dimension in autonomous systems.[7] A variety of perspectives of this nascent field can be found in the collected edition Machine Ethics[8] that stems from that symposium.
In 2007, AI magazine published "Machine Ethics: Creating an Ethical Intelligent Agent",[9] an article that discussed the importance of machine ethics, the need for machines that represent ethical principles explicitly, and challenges facing those working on machine ethics. It also demonstrated that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of ethical judgments and use that principle to guide its behavior.
In 2009, Oxford University Press published Moral Machines, Teaching Robots Right from Wrong,[10] which it advertised as "the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics." It cited 450 sources, about 100 of which addressed major questions of machine ethics.
In 2011, Cambridge University Press published a collection of essays about machine ethics edited by Michael and Susan Leigh Anderson,[8] who also edited a special issue of IEEE Intelligent Systems on the topic in 2006.[11] The collection focuses on the challenges of adding ethical principles to machines.[12]
In 2014, the US Office of Naval Research announced that it would distribute $7.5 million in grants over five years to university researchers to study questions of machine ethics as applied to autonomous robots,[13] and Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, which raised machine ethics as the "most important...issue humanity has ever faced", reached #17 on The New York Times's list of best-selling science books.[14]
In 2016 the European Parliament published a paper[15] to encourage the Commission to address robots' legal status.[16] The paper includes sections about robots' legal liability, in which it is argued that their liability should be proportional to their level of autonomy. The paper also discusses how many jobs could be taken by AI robots.[17]
In 2019 the Proceedings of the IEEE published a special issue on Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems, edited by Alan Winfield, Katina Michael, Jeremy Pitt and Vanessa Evers.[18] "The issue includes papers describing implicit ethical agents, where machines are designed to avoid unethical outcomes, as well as explicit ethical agents, or machines that either encode or learn ethics and determine actions based on those ethics".[19]
Areas of focus
AI control problem
Some scholars, such as Bostrom and AI researcher
This presents the
Algorithms and training
AI paradigms have been debated, especially their efficacy and bias. Bostrom and
In 2009, in an experiment at the Ecole Polytechnique Fédérale of Lausanne's Laboratory of Intelligent Systems, AI robots were programmed to cooperate with each other and tasked with searching for a beneficial resource while avoiding a poisonous one.[24] During the experiment, the robots were grouped into clans, and the successful members' digital genetic code was used for the next generation, a type of algorithm known as a genetic algorithm. After 50 successive generations in the AI, one clan's members discovered how to distinguish the beneficial resource from the poisonous one. The robots then learned to lie to each other in an attempt to hoard the beneficial resource from other robots.[24] In the same experiment, the same robots also learned to behave selflessly and signaled danger to other robots, and died to save other robots.[22] Machine ethicists have questioned the experiment's implications. In the experiment, the robots' goals were programmed to be "terminal", but human motives typically require never-ending learning.
Autonomous weapons systems
In 2009, academics and technical experts attended a conference to discuss the potential impact of robots and computers and the impact of the possibility that they could become self-sufficient and able to make their own decisions. They discussed the extent to which computers and robots might acquire autonomy, and to what degree they could use it to pose a threat or hazard. They noted that some machines have acquired various forms of semi-autonomy, including the ability to find power sources on their own and to independently choose targets to attack with weapons. They also noted that some computer viruses can evade elimination and have achieved "cockroach intelligence". They noted that self-awareness as depicted in science fiction is probably unlikely, but that there are other potential hazards and pitfalls.[25]
Some experts and academics have questioned the use of robots in military combat, especially robots with a degree of autonomy.[26] The U.S. Navy funded a report that indicates that as military robots become more complex, we should pay greater attention to the implications of their ability to make autonomous decisions.[27][28] The president of the Association for the Advancement of Artificial Intelligence has commissioned a study of this issue.[29]
Integration of artificial general intelligences with society
Preliminary work has been conducted on methods of integrating artificial general intelligences (full ethical agents as defined above) with existing legal and social frameworks. Approaches have focused on their legal position and rights.[30]
Machine learning bias
The U.S. judicial system has begun using
In 2016, the
Ethical frameworks and practices
Practices
In March 2018, in an effort to address rising concerns over machine learning's impact on human rights, the World Economic Forum and Global Future Council on Human Rights published a white paper with detailed recommendations on how best to prevent discriminatory outcomes in machine learning.[36] The World Economic Forum developed four recommendations based on the UN Guiding Principles of Human Rights to help address and prevent discriminatory outcomes in machine learning:[36]
- Active inclusion: Development and design of machine learning applications must actively seek a diversity of input, especially of the norms and values of populations affected by the output of AI systems.
- Fairness: People involved in conceptualizing, developing, and implementing machine learning systems should consider which definition of fairness best applies to their context and application, and prioritize it in the machine learning system's architecture and evaluation metrics.
- Right to understanding: Involvement of machine learning systems in decision-making that affects individual rights must be disclosed, and the systems must be able to explain their decision-making in a way that is understandable to end users and reviewable by a competent human authority. Where this is impossible and rights are at stake, leaders in the design, deployment, and regulation of machine learning technology must question whether it should be used.
- Access to redress: Leaders, designers, and developers of machine learning systems are responsible for identifying the potential negative human rights impacts of their systems. They must make visible avenues for redress for those affected by disparate impacts, and establish processes for the timely redress of any discriminatory outputs.
In January 2020, Harvard University's Berkman Klein Center for Internet and Society published a meta-study of 36 prominent sets of principles for AI, identifying eight key themes: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values.[37] Researchers at the Swiss Federal Institute of Technology in Zurich conducted a similar meta-study in 2019.[38]
Approaches
There have been several attempts to make ethics computable, or at least
One thought experiment focuses on a Genie Golem with unlimited powers presenting itself to the reader. This Genie declares that it will return in 50 years and demands that it be provided with a definite set of morals it will then immediately act upon. This experiment's purpose is to spark discourse over how best to handle defining sets of ethics that computers may understand.[47]
Some recent work attempts to reconstruct AI morality and control more broadly as a problem of mutual contestation between AI as a Foucauldian subjectivity on the one hand and humans or institutions on the other hand, all within a disciplinary apparatus. Certain desiderata need to be fulfilled: embodied self-care, embodied intentionality, imagination and reflexivity, which together would condition AI's emergence as an ethical subject capable of self-conduct.[48]
In fiction
In science fiction, movies and novels have played with the idea of sentient robots and machines.
Asimov considered the issue in the 1950s in
Related fields
- Affective computing
- Formal ethics[51]
- Bioethics
- Computational theory of mind
- Computer ethics
- Ethics of artificial intelligence
- Moral psychology
- Philosophy of artificial intelligence
- Philosophy of mind
See also
- Artificial intelligence
- Automating medical decision-support
- Google car
- Military robot
- Machine Intelligence Research Institute
- Robot ethics
- Space law
- Watson project for automating medical decision-support
Notes
- S2CID 831873.
- ^ Boyles, Robert James. "A Case for Machine Ethics in Modeling Human-Level Intelligent Agents" (PDF). Kritike. Retrieved 1 November 2019.
- ^ Moor, James M. (2009). "Four Kinds of Ethical Robots". Philosophy Now.
- .
- ^ Anderson, M., Anderson, S., and Armen, C. (2004) "Towards Machine Ethics" in Proceedings of the AAAI Workshop on Agent Organization: Theory and Practice, AAAI Press [1]
- ^ AAAI Workshop on Agent Organization: Theory and Practice, AAAI Press
- ^ "Papers from the 2005 AAAI Fall Symposium". Archived from the original on 2014-11-29.
- ^ ISBN 978-0-521-11235-2.
- ^ a b Anderson, M. and Anderson, S. (2007). Creating an Ethical Intelligent Agent. AI Magazine, Volume 28(4).
- ISBN 9780195374049.
- S2CID 9570832. Archived from the originalon 2011-11-26.
- S2CID 5613776.
- ^ Tucker, Patrick (13 May 2014). "Now The Military Is Going To Build Robots That Have Morals". Defense One. Retrieved 9 July 2014.
- ^ "Best Selling Science Books". New York Times. September 8, 2014. Retrieved 9 November 2014.
- ^ "European Parliament, Committee on Legal Affairs. Draft Report with recommendations to the Commission on Civil Law Rules on Robotics". European Commission. Retrieved January 12, 2017.
- ^ Wakefield, Jane (2017-01-12). "MEPs vote on robots' legal status – and if a kill switch is required". BBC News. Retrieved 12 January 2017.
- ^ "European Parliament resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics". European Parliament. Retrieved 8 November 2019.
- .
- ^ "Proceedings of the IEEE Addresses Machine Ethics". IEEE Standards Association.
- ISBN 978-0199678112.
- Cambridge Press. Archived from the original(PDF) on 2016-03-04. Retrieved 2011-06-28.
- ^ a b Santos-Lang, Chris (2002). "Ethics for Artificial Intelligences". Archived from the original on 2011-12-03.
- ISBN 978-3-319-08107-6.
- ^ a b Fox, Stuart (August 18, 2009). "Evolving Robots Learn To Lie To Each Other". Popular Science.
- ^ Markoff, John (July 25, 2009). "Scientists Worry Machines May Outsmart Man". New York Times.
- ^ Palmer, Jason (3 August 2009). "Call for debate on killer robots". BBC News.
- ^ Science New Navy-funded Report Warns of War Robots Going "Terminator" Archived 2009-07-28 at the Wayback Machine, by Jason Mick (Blog), dailytech.com, February 17, 2009.
- ^ Flatley, Joseph L. (February 18, 2009). "Navy report warns of robot uprising, suggests a strong moral compass". Engadget.
- ^ AAAI Presidential Panel on Long-Term AI Futures 2008–2009 Study, Association for the Advancement of Artificial Intelligence, Accessed 7/26/09.
- ISSN 0031-8949.
- ^ a b Crawford, Kate (25 June 2016). "Artificial Intelligence's White Guy Problem". The New York Times.
- ^ a b c Julia Angwin; Surya Mattu; Jeff Larson; Lauren Kircher (23 May 2016). "Machine Bias: There's Software Used Across the Country to Predict Future Criminals. And it's Biased Against Blacks". ProPublica.
- .
- ^ Executive Office of the President (May 2016). "Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights" (PDF). Obama White House.
- ^ "Big Risks, Big Opportunities: the Intersection of Big Data and Civil Rights". Obama White House. 4 May 2016.
- ^ a b "How to Prevent Discriminatory Outcomes in Machine Learning". World Economic Forum. 12 March 2018. Retrieved 2018-12-11.
- S2CID 214464355.
- S2CID 201827642.
- ISBN 9780511978036
- ^ Powers, Thomas M. (2011): Prospects for a Kantian Machine. In: Machine Ethics, ed. Michael Anderson, Susan Leigh Anderson. New York: Oxford University Press. pp.464–475.
- ^ Muehlhauser, Luke, Helm, Louie (2012): Intelligence Explosion and Machine Ethics.
- ^ Yudkowsky, Eliezer (2004): Coherent Extrapolated Volition.
- ^ Guarini, Marcello (2011): Computational Neural Modeling and the Philosophy of Ethics. Reflections on the Particularism-Generalism Debate. In: Machine Ethics, ed. Michael Anderson, Susan Leigh Anderson. New York: Oxford University Press. pp.316–334.
- ].
- S2CID 11588399.
- ^ Wakefield, Jane (24 March 2016). "Microsoft chatbot is taught to swear on Twitter". BBC News. Retrieved 2016-04-17.
- ^ Nazaretyan, A. (2014). A. H. Eden, J. H. Moor, J. H. Søraker and E. Steinhart (eds): Singularity Hypotheses: A Scientific and Philosophical Assessment. Minds & Machines, 24(2), pp.245–248.
- ISSN 0951-5666.
- ^ Brundage, Miles; Winterton, Jamie (17 March 2015). "Chappie and the Future of Moral Machines". Slate. Retrieved 30 October 2019.
- ISBN 978-0-553-38256-3.
- ^ Ganascia, Jean-Gabriel. "Ethical system formalization using non-monotonic logics." Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 29. No. 29. 2007.
References
- Wallach, Wendell; Allen, Colin (November 2008). Moral Machines: Teaching Robots Right from Wrong. US: Oxford University Press.
- Anderson, Michael; Anderson, Susan Leigh, eds (July 2011). Machine Ethics. Cambridge University Press.
- Storrs Hall, J. (May 30, 2007). Beyond AI: Creating the Conscience of the Machine Prometheus Books.
- Moor, J. (2006). The Nature, Importance, and Difficulty of Machine Ethics. IEEE Intelligent Systems, 21(4), pp. 18–21.
- Anderson, M. and Anderson, S. (2007). Creating an Ethical Intelligent Agent. AI Magazine, Volume 28(4).
Further reading
- Hagendorff, Thilo (2021). Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning. Minds and Machines, .
- Anderson, Michael; Anderson, Susan Leigh, eds (July/August 2006). "Special Issue on Machine Ethics". IEEE Intelligent Systems 21 (4): 10–63.
- Bendel, Oliver (December 11, 2013). Considerations about the Relationship between Animal and Machine Ethics. AI & SOCIETY, .
- Dabringer, Gerhard, ed. (2010). "Ethical and Legal Aspects of Unmanned Systems. Interviews". Austrian Ministry of Defence and Sports, Vienna 2010, ISBN 978-3-902761-04-0.
- Gardner, A. (1987). An Artificial Approach to Legal Reasoning. Cambridge, MA: MIT Press.
- Georges, T. M. (2003). Digital Soul: Intelligent Machines and Human Values. Cambridge, MA: Westview Press.
- Singer, P.W. (December 29, 2009). Wired for War: The Robotics Revolution and Conflict in the 21st Century: Penguin.
- Winfield, A., Michael, K., Pitt, J. and Evers, V. (March 2019). Special Issue on Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems. Proceedings of the IEEE. 107 (3): 501–615,
External links
- Machine Ethics, Interdisciplinary project on machine ethics.
- The Machine Ethics Podcast, Podcast discussing Machine Ethics, AI and Tech ethics.