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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 the moral behavior of artificially intelligent beings. Machine ethics contrasts with roboethics, which is concerned with the moral behavior of humans as they design, construct, use and treat such beings. Machine ethics should not be confused with computer ethics, which focuses on professional behavior towards computers and information.
Before the 21st century the ethics of machines had largely been the subject of science fiction literature, mainly due to computing and artificial intelligence (AI) limitations. These limitations are being overcome through advances in theory and hardware resulting in a renewed focus on the field of artificial intelligence making Machine Ethics a bona fide field of research. The first use of the term seems to be by Mitchell Waldrop in the 1987 AI Magazine article "A Question of Responsibility":
"However, 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."
The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics:
"Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics."
Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics" that stems from the AAAI Fall 2005 Symposium on Machine Ethics.
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 some 450 sources, about 100 of which addressed major questions of machine ethics. Few sources were written before the 21st century largely because any form of artificial intelligence was nonexistent.
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.
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 Superintelligence: Paths, Dangers, Strategies, which raised machine ethics as the "most important...issue humanity has ever faced," reached #17 on the New York Times list of best selling science books.
Moor distinguished between:
- Ethical impact agents, defined as machine systems carrying an ethical impact.
- Implicit ethical agents, with actions constrained to avoid unethical outcomes.
- Explicit ethical agents, which have algorithms to be consciously ethical.
- Full ethical agents, allowing that agents of the above three types derive their ethics from their human developers. (See artificial systems and moral responsibility.)
In 2009, academics and technical experts attended a conference to discuss the potential impact of robots and computers and the impact of the hypothetical possibility that they could become self-sufficient and able to make their own decisions. They discussed the possibility and the extent to which computers and robots might be able to acquire any level of autonomy, and to what degree they could use such abilities to possibly pose any threat or hazard. They noted that some machines have acquired various forms of semi-autonomy, including being able to find power sources on their own and being able 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 were other potential hazards and pitfalls.
Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions. The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions. The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue. They point to programs like the Language Acquisition Device which can emulate human interaction.
Nick Bostrom and Eliezer Yudkowsky have argued for decision trees (such as ID3) over neural networks and genetic algorithms on the grounds that decision trees obey modern social norms of transparency and predictability (e.g. stare decisis), while Chris Santos-Lang argued in the opposite direction on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these particular norms has been essential in making humans less vulnerable than machines to criminal "hackers".
Training and instruction
In 2009, during an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of Lausanne in Switzerland, robots that were programmed to cooperate with each other in searching out a beneficial resource and avoiding a poisonous one eventually learned to lie to each other in an attempt to hoard the beneficial resource.
One problem in this case may have been that the goals were "terminal" (i.e. in contrast, ultimate human motives typically have a quality of requiring never-ending learning). The robots were grouped into clans and the successful members' digital genetic code was used for the next generation. After 50 successive generations, one clan's members discovered how to indicate food instead of poison. At the same time there were selfless robots who signaled danger and died to save others.These adaptations could have been the result of the genetic code or their human creators. (See also Genetic algorithm.)
In Moral Machines: Teaching Robots Right from Wrong, Wendell Wallach and Colin Allen conclude that attempts to teach robots right from wrong will likely advance understanding of human ethics by motivating humans to address gaps in modern normative theory and by providing a platform for experimental investigation.
Several attempts have been made to make ethics computable or at least formal. Whereas Isaac Asimov's Three Laws of Robotics are usually not considered to be suitable for an artificial moral agent, it has been studied whether Kant's categorical imperative can be used. However, it has been pointed out that human value is in some aspects very complex. A way to explicitly surmount this difficulty is to receive human values directly from them through some mechanism, for example by learning them.
Another approach is to base current ethical considerations on previous similar situations. This is called casuistry and it could be implemented through research on the internet. The consensus from a million past decisions would lead to a new decision that is democracy dependent. This could, however, lead to decisions that reflect biases and unethical behaviors exhibited in society. The negative effects of this approach can be seen in Microsoft's Tay (bot), where the chatterbot learned to repeat racist and sexually charged messages sent by Twitter users.
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 that it will then immediately act upon. The purpose of this experiment is to initiate a discourse over how best to handle defining complete set of ethics that computers may understand.
Isaac 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 the boundaries of his three laws to see where they would break down, or where they would create paradoxical or unanticipated behavior. His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances.
- Affective computing
- Computational theory of mind
- Computer ethics
- Ethics of artificial intelligence
- Moral psychology
- Philosophy of artificial intelligence
- Philosophy of mind
- Artificial intelligence
- Automating medical decision-support
- Google car – will it protect passengers over pedestrians?
- Military robot
- Machine Intelligence Research Institute
- Watson project for automating medical decision-support
- Tay (bot)
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- Santos-Lang, Christopher (2014). "Moral Ecology Approaches to Machine Ethics". In van Rysewyk, Simon; Pontier, Matthijs. Machine Medical Ethics (PDF). Switzerland: Springer. pp. 111–127. doi:10.1007/978-3-319-08108-3_8.
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- 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.
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- Anderson, Michael; Anderson, Susan Leigh, eds (July 2011). Machine Ethics. Cambridge University Press.
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- Anderson, M. and Anderson, S. (2007). Creating an Ethical Intelligent Agent. AI Magazine, Volume 28(4).
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- Machine Ethics, Hartford University links to conferences, articles and researchers.