“ETHICS: Aligning AI With Shared Human Values”, Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt2020-08-05 (ML dataset, GPT, AI safety; backlinks; similar)⁠:

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.

[cf. “Language Models have a Moral Dimension”, Schramowski et al 2021.]

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