AI-Facilitated Collective Judgements

2025-03-06

AI Summary

This paper addresses the role of AI in aggregating collective preferences and facilitating consensus formation. It argues for conceptualizing AI as a “discovery tool” rather than a “decision agent,” emphasizing its contextual embedding within democratic decision-making processes and its respect for the situatedness and normative sensitivity of preferences. Methodologically, the study integrates computational social choice, explainable AI (XAI), preference modeling, and democratic theory to critically reconstruct historical public opinion polling paradigms. Key contributions include: (1) design principles for AI-augmented judgment that balance technical feasibility with democratic legitimacy; (2) identification of three high-risk application patterns—substitutive decision-making, covert power transfer, and political outcome rationalization; and (3) concrete governance boundaries to safeguard democratic integrity. By bridging algorithmic design and democratic values, this work provides both a theoretical framework and practical guidance for deploying AI in support of collective judgment.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Knowledge Representation and Reasoning: Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This article unpacks the design choices behind longstanding and newly proposed computational frameworks aimed at finding common grounds across collective preferences and examines their potential future impacts, both technically and normatively. It begins by situating AI-assisted preference elicitation within the historical role of opinion polls, emphasizing that preferences are shaped by the decision-making context and are seldom objectively captured. With that caveat in mind, we explore AI-facilitated collective judgment as a discovery tool for fostering reasonable representations of a collective will, sense-making, and agreement-seeking. At the same time, we caution against dangerously misguided uses, such as enabling binding decisions, fostering gradual disempowerment or post-rationalizing political outcomes.

Problem

• Exploring AI frameworks for collective preference alignment
• Examining AI's role in shaping and capturing collective judgments
• Addressing risks of AI in decision-making and political outcomes

Innovation

• AI-assisted preference elicitation for collective decisions
• Discovery tool for reasonable collective will representation
• Caution against misuse in binding political decisions