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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 design machines that use artificial intelligence (AI), otherwise known as AI agents. 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.
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.
In 2004, Towards Machine EthicsAnderson, 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] was presented at the AAAI Workshop on Agent Organizations: Theory and Practice. 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. A variety of perspectives of this nascent field can be found in the collected edition Machine Ethics (2011). 9780521112352, Cambridge University Press. ISBN 9780521112352 that stems from that symposium.
In 2007, AI magazine published "Machine Ethics: Creating an Ethical Intelligent Agent",Anderson, M. and Anderson, S. (2007). Creating an Ethical Intelligent Agent. AI Magazine, Volume 28(4). 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, 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, who also edited a special issue of IEEE Intelligent Systems on the topic in 2006. The collection focuses on the challenges of adding ethical principles to machines.
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, and Nick Bostrom's , which raised machine ethics as the "most important...issue humanity has ever faced", reached #17 on The New York Times
In 2016 the European Parliament published a paper to encourage the Commission to address robots' legal status. 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.
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. "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".
This presents the AI control problem: how to build an intelligent agent that will aid its creators without inadvertently building a superintelligence that will harm them. The danger of not control right "the first time" is that a superintelligence may be able to seize power over its environment and prevent us from shutting it down. Potential AI control strategies include "capability control" (limiting an AI's ability to influence the world) and "motivational control" (one way of building an AI whose goals are AI alignment with human or optimal values). A number of organizations are researching the AI control problem, including the Future of Humanity Institute, the Machine Intelligence Research Institute, the Center for Human-Compatible Artificial Intelligence, and the Future of Life Institute.
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. 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. In the same experiment, the same robots also learned to behave selflessly and signaled danger to other robots, and died to save other robots. 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.
Some experts and academics have questioned the use of robots in military combat, especially robots with a degree of autonomy. The U.S. Navy funded a report that indicates that as Military robot become more complex, we should pay greater attention to the implications of their ability to make autonomous decisions. Science New Navy-funded Report Warns of War Robots Going "Terminator" , by Jason Mick (Blog), dailytech.com, February 17, 2009. The president of the Association for the Advancement of Artificial Intelligence has commissioned a study of this issue. AAAI Presidential Panel on Long-Term AI Futures 2008–2009 Study, Association for the Advancement of Artificial Intelligence, Accessed 7/26/09.
The U.S. judicial system has begun using quantitative risk assessment software when making decisions related to releasing people on bail and sentencing in an effort to be fairer and reduce the imprisonment rate. These tools analyze a defendant's criminal history, among other attributes. In a study of 7,000 people arrested in Broward County, Florida, only 20% of people predicted to commit a crime using the county's risk assessment scoring system proceeded to commit a crime. A 2016 ProPublica report analyzed recidivism risk scores calculated by one of the most commonly used tools, the Northpointe COMPAS system, and looked at outcomes over two years. The report found that only 61% of those deemed high-risk committed additional crimes during that period. The report also flagged that African-American defendants were far more likely to be given high-risk scores than their white counterparts. It has been argued that such pretrial risk assessments violate Equal Protection rights on the basis of race, due to factors including possible discriminatory intent by the algorithm itself, under a theory of partial legal capacity for artificial intelligences.
In 2016, the Obama administration's Big Data Working Group—an overseer of various big-data regulatory frameworks—released reports warning of "the potential of encoding discrimination in automated decisions" and calling for "equal opportunity by design" for applications such as credit scoring. The reports encourage discourse among policy-makers, citizens, and academics alike, but recognize that no solution yet exists for the encoding of bias and discrimination into algorithmic systems.
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. Researchers at the ETH Zurich conducted a similar meta-study in 2019.
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.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.
Some recent work attempts to reconstruct AI morality and control more broadly as a problem of mutual contestation between AI as a Michel Foucault 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.
Neill Blomkamp's Chappie (2015) enacts a scenario of being able to transfer one's consciousness into a computer. Alex Garland's 2014 film Ex Machina follows an android with artificial intelligence undergoing a variation of the Turing Test, a test administered to a machine to see whether its behavior can be distinguished from that of a human. Films such as The Terminator (1984) and The Matrix (1999) incorporate the concept of machines turning on their human masters.
Asimov considered the issue in the 1950s in I, Robot. At the insistence of his editor John W. Campbell Jr., he proposed the Three Laws of Robotics to govern artificially intelligent systems. Much of his work was then spent testing his three laws' boundaries to see where they break down or create paradoxical or unanticipated behavior. His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances. Philip K. Dick's 1968 novel Do Androids Dream of Electric Sheep? explores what it means to be human. In his post-apocalyptic scenario, he questions whether empathy is an entirely human characteristic. The book is the basis for the 1982 science-fiction film Blade Runner.
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