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In many contexts, lying -- the use of verbal falsehoods to deceive -- isharmful. While lying has traditionally been a human affair, AI systems thatmake sophisticated verbal statements are becoming increasingly prevalent. Thisraises the question of how we should limit the harm caused by AI "lies" (i.e.falsehoods that are actively selected for). Human truthfulness is governed bysocial norms and by laws (against defamation, perjury, and fraud). Differencesbetween AI and humans present an opportunity to have more precise standards oftruthfulness for AI, and to have these standards rise over time. This couldprovide significant benefits to public epistemics and the economy, and mitigaterisks of worst-case AI futures.  Establishing norms or laws of AI truthfulness will require significant workto: (1) identify clear truthfulness standards; (2) create institutions that canjudge adherence to those standards; and (3) develop AI systems that arerobustly truthful.  Our initial proposals for these areas include: (1) a standard of avoiding"negligent falsehoods" (a generalisation of lies that is easier to assess); (2)institutions to evaluate AI systems before and after real-world deployment; and(3) explicitly training AI systems to be truthful via curated datasets andhuman interaction.  A concerning possibility is that evaluation mechanisms for eventualtruthfulness standards could be captured by political interests, leading toharmful censorship and propaganda. Avoiding this might take careful attention.And since the scale of AI speech acts might grow dramatically over the comingdecades, early truthfulness standards might be particularly important becauseof the precedents they set.

Truthful AI: Developing and governing AI that does not lie