Moral Decision Making Frameworks for Artificial Intelligence
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Conitzer, V; Sinnott-Armstrong, W; Schaich Borg, J; Deng, Y; Kramer, M
The generality of decision and game theory has enabled domain-independent progress in AI research. For example, a better algorithm for finding good policies in (PO)MDPs can be instantly used in a variety of applications. But such a general theory is lacking when it comes to moral decision making. For AI applications with a moral component, are we then forced to build systems based on many ad-hoc rules? In this paper we discuss possible ways to avoid this conclusion.
Duke Scholars
Conference Name
Conference on Artificial Intelligence (AAAI-17)
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Conitzer, V., Sinnott-Armstrong, W., Schaich Borg, J., Deng, Y., & Kramer, M. (n.d.). Moral Decision Making Frameworks for Artificial Intelligence. Presented at the Conference on Artificial Intelligence (AAAI-17).
Conitzer, V., W. Sinnott-Armstrong, J. Schaich Borg, Y. Deng, and M. Kramer. “Moral Decision Making Frameworks for Artificial Intelligence,” n.d.
Conitzer V, Sinnott-Armstrong W, Schaich Borg J, Deng Y, Kramer M. Moral Decision Making Frameworks for Artificial Intelligence. In.
Conitzer, V., et al. Moral Decision Making Frameworks for Artificial Intelligence.
Conitzer V, Sinnott-Armstrong W, Schaich Borg J, Deng Y, Kramer M. Moral Decision Making Frameworks for Artificial Intelligence.
Conference Name
Conference on Artificial Intelligence (AAAI-17)