mlsanityData to 7 Oct 2026

Inclusive Artificial Intelligence

arXiv 2212.12633 ↗First posted 24 Dec 2022cs.LG cs.AIcited by 1

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Abstract (arXiv)

Prevailing methods for assessing and comparing generative AIs incentivize responses that serve a hypothetical representative individual. Evaluating models in these terms presumes homogeneous preferences across the population and engenders selection of agglomerative AIs, which fail to represent the diverse range of interests across individuals. We propose an alternative evaluation method that instead prioritizes inclusive AIs, which provably retain the requisite knowledge not only for subsequent response customization to particular segments of the population but also for utility-maximizing decisions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 24 Dec 2022.

Builds on

24 of 35 references.

References · 24

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  3. 2017Deep reinforcement learning from human preferencescited by 3,846
  4. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  5. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  6. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  7. 2020Learning to summarize from human feedbackcited by 2,562
  8. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  9. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  10. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,023
  11. 2021Ethical and social risks of harm from Language Modelscited by 957
  12. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
12 more
  1. 2020Aligning AI With Shared Human Valuescited by 706
  2. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 702
  3. 2022Improving alignment of dialogue agents via targeted human judgementscited by 527
  4. 2022Teaching language models to support answers with verified quotescited by 290
  5. 2021Recursively Summarizing Books with Human Feedbackcited by 287
  6. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  7. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  8. 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Modelscited by 159
  9. 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 137
  10. 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 89
  11. 2022In conversation with Artificial Intelligence: aligning language models with human valuescited by 61
  12. 2022Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedbackcited by 26

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023RLHF and IIA: Perverse Incentivescited by 6