Aligning AI With Shared Human ValuesDownload PDF

28 Sept 2020, 15:50 (modified: 04 Mar 2021, 21:44)ICLR 2021 PosterReaders: Everyone
Keywords: value learning, human preferences, alignment
Abstract: We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
One-sentence Summary: We approach a longstanding problem in machine ethics provide evidence that it is soluble.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
12 Replies

Loading