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As AI systems become an increasing part of people's everyday lives, itbecomes ever more important that they understand people's ethical norms.Motivated by descriptive ethics, a field of study that focuses on people'sdescriptive judgments rather than theoretical prescriptions on morality, weinvestigate a novel, data-driven approach to machine ethics.  We introduce Scruples, the first large-scale dataset with 625,000 ethicaljudgments over 32,000 real-life anecdotes. Each anecdote recounts a complexethical situation, often posing moral dilemmas, paired with a distribution ofjudgments contributed by the community members. Our dataset presents a majorchallenge to state-of-the-art neural language models, leaving significant roomfor improvement. However, when presented with simplified moral situations, theresults are considerably more promising, suggesting that neural models caneffectively learn simpler ethical building blocks.  A key take-away of our empirical analysis is that norms are not alwaysclean-cut; many situations are naturally divisive. We present a new method toestimate the best possible performance on such tasks with inherently diverselabel distributions, and explore likelihood functions that separate intrinsicfrom model uncertainty.

Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes