Minds and Machines 30 (2):219-246 (2020)

Julia Haas
Rhodes College
I describe a suite of reinforcement learning environments in which artificial agents learn to value and respond to moral content and contexts. I illustrate the core principles of the framework by characterizing one such environment, or “gridworld,” in which an agent learns to trade-off between monetary profit and fair dealing, as applied in a standard behavioral economic paradigm. I then highlight the core technical and philosophical advantages of the learning approach for modeling moral cognition, and for addressing the so-called value alignment problem in AI.
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DOI 10.1007/s11023-020-09524-9
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References found in this work BETA

The Weirdest People in the World?Joseph Henrich, Steven J. Heine & Ara Norenzayan - 2010 - Behavioral and Brain Sciences 33 (2-3):61-83.

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Moral Rationalism on the Brain.Joshua May - forthcoming - Mind and Language.

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