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A theory of appropriateness with applications to generative artificial intelligence
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A theory of appropriateness with applications to generative artificial intelligence
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What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with our family, and yet another in the office. Likewise for AI, appropriate behavior for a comedy-writing assistant is not the same as appropriate behavior for a customer-service representative. What determines which actions are appropriate in which contexts? And what causes these standards to change over time? Since all judgments of AI appropriateness are ultimately made by humans, we need to understand how appropriateness guides human decision making in order to properly evaluate AI decision making and improve it. This paper presents a theory of appropriateness: how it functions in human society, how it may be implemented in the brain, and what it means for responsible deployment of generative AI technology.
Forward citations
Cited by 4 Pith papers
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SCENE: Recognizing Social Norms and Sanctioning in Group Chats
SCENE is a new benchmark for testing LLMs on recognizing implicit social norms and adapting to sanctions in multi-party group chats.
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Stabilising Generative Models of Attitude Change
Researchers rendered cognitive dissonance, self-consistency, and self-perception theories as generative simulations that reproduce classic experimental behavioral patterns after iterative manual stabilization.
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Stabilising Generative Models of Attitude Change
Classic attitude-change theories can be rendered as generative agents that match known experimental patterns, but only after manual stabilisation that surfaces undocumented operational commitments.
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Computational Hermeneutics: Evaluating generative AI as a cultural technology
Generative AI should be evaluated through computational hermeneutics using iterative, human-inclusive benchmarks that measure cultural context rather than isolated model outputs.
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