The reviewed record of science sign in
Pith

arxiv: 2412.19010 · v1 · pith:BCOXV4KG · submitted 2024-12-26 · cs.AI

A theory of appropriateness with applications to generative artificial intelligence

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:BCOXV4KGrecord.jsonopen to challenge →

classification cs.AI
keywords appropriatenesswhatappropriateanotherbehaviordecisiongenerativehuman
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SCENE: Recognizing Social Norms and Sanctioning in Group Chats

    cs.CL 2026-05 unverdicted novelty 7.0

    SCENE is a new benchmark for testing LLMs on recognizing implicit social norms and adapting to sanctions in multi-party group chats.

  2. Stabilising Generative Models of Attitude Change

    cs.AI 2026-04 unverdicted novelty 6.0

    Researchers rendered cognitive dissonance, self-consistency, and self-perception theories as generative simulations that reproduce classic experimental behavioral patterns after iterative manual stabilization.

  3. Computational Hermeneutics: Evaluating generative AI as a cultural technology

    cs.AI 2026-03 unverdicted novelty 5.0

    Generative AI should be evaluated through computational hermeneutics using iterative, human-inclusive benchmarks that measure cultural context rather than isolated model outputs.