The Elephant in the Room -- Why AI Safety Demands Diverse Teams
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Abstract (arXiv)
We consider that existing approaches to AI "safety" and "alignment" may not be using the most effective tools, teams, or approaches. We suggest that an alternative and better approach to the problem may be to treat alignment as a social science problem, since the social sciences enjoy a rich toolkit of models for understanding and aligning motivation and behavior, much of which could be repurposed to problems involving AI models, and enumerate reasons why this is so. We introduce an alternate alignment approach informed by social science tools and characterized by three steps: 1. defining a positive desired social outcome for human/AI collaboration as the goal or "North Star," 2. properly framing knowns and unknowns, and 3. forming diverse teams to investigate, observe, and navigate emerging challenges in alignment.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 55 references that are in the map. First posted on arXiv 7 May 2024.
Builds on
10 of 55 references.
References · 10
- 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
- 2016Concrete Problems in AI Safetycited by 1,582
- 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
- 2020Artificial Intelligence, Values and Alignmentcited by 443
- 2023AI Alignment: A Comprehensive Surveycited by 256
- 2023Emotional Intelligence of Large Language Modelscited by 58
- 2023Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBenchcited by 54
- 2023Large Language Models as Superpositions of Cultural Perspectivescited by 48
- 2018AGI Safety Literature Reviewcited by 28
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