Cited By
View all- Zhi-Xuan TCarroll MFranklin MAshton H(2024)Beyond Preferences in AI AlignmentPhilosophical Studies10.1007/s11098-024-02249-wOnline publication date: 9-Nov-2024
Collective Constitutional AI: Aligning a Language Model with Public Input
Authors: 
Saffron Huang, Divya Siddarth, Liane Lovitt, Thomas I. Liao, Esin Durmus, Alex Tamkin, Deep GanguliAuthors Info & Claims
Pages 1395 - 1417
Abstract
There is growing consensus that language model (LM) developers should not be the sole deciders of LM behavior, creating a need for methods that enable the broader public to collectively shape the behavior of LM systems that affect them. To address this need, we present Collective Constitutional AI (CCAI): a multi-stage process for sourcing and integrating public input into LMs—from identifying a target population to sourcing principles to training and evaluating a model. We demonstrate the real-world practicality of this approach by creating what is, to our knowledge, the first LM fine-tuned with collectively sourced public input and evaluating this model against a baseline model trained with established principles from a LM developer. Our quantitative evaluations demonstrate several benefits of our approach: the CCAI-trained model shows lower bias across nine social dimensions compared to the baseline model, while maintaining equivalent performance on language, math, and helpful-harmless evaluations. Qualitative comparisons of the models suggest that the models differ on the basis of their respective constitutions, e.g., when prompted with contentious topics, the CCAI-trained model tends to generate responses that reframe the matter positively instead of a refusal. These results demonstrate a promising, tractable pathway toward publicly informed development of language models.
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June 2024
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ISBN:9798400704505
DOI:10.1145/3630106
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FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency
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View all- Zhi-Xuan TCarroll MFranklin MAshton H(2024)Beyond Preferences in AI AlignmentPhilosophical Studies10.1007/s11098-024-02249-wOnline publication date: 9-Nov-2024
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