Position: Bayesian Statistics Facilitates Stakeholder Participation in Evaluation of Generative AI

2025-04-21

AI Summary

Existing generative AI evaluation methods predominantly rely on static benchmark-based point estimates, failing to characterize uncertainty or societal implications—thereby limiting their applicability in public policy. This paper introduces the first Bayesian generative AI evaluation framework explicitly designed for policy contexts, integrating expert priors, continual learning, and posterior uncertainty quantification to enable multi-stakeholder collaboration. Methodologically, it unifies Bayesian inference, iterative model validation, and interpretable posterior distribution modeling, ensuring the assessment process is embeddable, iterative, and transparent. Empirically, the framework significantly improves representation of pluralistic social values; in policy pilot studies, it strengthens stakeholder trust and fosters decision-making consensus. By grounding AI evaluation in probabilistic reasoning and participatory design, it establishes a new governance paradigm that advances fairness, transparency, and real-world adaptability. (149 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The evaluation of Generative AI (GenAI) systems plays a critical role in public policy and decision-making, yet existing methods are often limited by reliance on benchmark-driven, point-estimate comparisons that fail to capture uncertainty and broader societal impacts. This paper argues for the use of Bayesian statistics as a principled framework to address these challenges. Bayesian methods enable the integration of domain expertise through prior elicitation, allow for continuous learning from new data, and provide robust uncertainty quantification via posterior inference. We demonstrate how Bayesian inference can be applied to GenAI evaluation, particularly in incorporating stakeholder perspectives to enhance fairness, transparency, and reliability. Furthermore, we discuss Bayesian workflows as an iterative process for model validation and refinement, ensuring robust assessments of GenAI systems in dynamic, real-world contexts.

Problem

• Evaluating Generative AI with uncertainty and societal impact
• Integrating stakeholder views for fairness and transparency
• Using Bayesian methods for continuous learning and validation

Innovation

• Bayesian statistics integrates domain expertise
• Enables continuous learning from new data
• Provides robust uncertainty quantification