Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration

2025-06-12

AI Summary

AI training data markets exhibit severe power asymmetries, wherein information producers—such as journalists, researchers, and creative professionals—lack bargaining power, leading to value extraction, excessive capital concentration, and degradation of the information commons. Method: This paper proposes a novel governance paradigm—“Information Producers’ Coalition + Trusted Data Intermediary”—and provides the first systematic theoretical and practical demonstration of how collective bargaining mechanisms can correct market failures in the AI era. It integrates federated data management tools, interpretable data valuation models, and institutionally embedded trusted intermediary architectures into a technology–institution co-design framework. Contribution/Results: The framework safeguards data sovereignty and ensures equitable remuneration for producers while preserving innovation incentives and long-term sustainability. It offers a globally applicable, normatively grounded, and technically feasible pathway for AI data governance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

This position paper argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, producers of information goods (such as journalists, researchers, and creative professionals) need to be able to collectively bargain with AI product builders in order to receive reasonable terms and a sustainable return on the informational value they contribute. We argue that without increased market coordination or collective bargaining on the side of these primary information producers, AI will exacerbate a large-scale"information market failure"that will lead not only to undesirable concentration of capital, but also to a potential"ecological collapse"in the informational commons. On the other hand, collective bargaining in the information economy can create market frictions and aligned incentives necessary for a pro-social, sustainable AI future. We provide concrete actions that can be taken to support a coalition-based approach to achieve this goal. For example, researchers and developers can establish technical mechanisms such as federated data management tools and explainable data value estimations, to inform and facilitate collective bargaining in the information economy. Additionally, regulatory and policy interventions may be introduced to support trusted data intermediary organizations representing guilds or syndicates of information producers.

Problem

• Address AI-driven power concentration in information markets
• Enable collective bargaining for fair value of information goods
• Prevent information market failure and ecological collapse

Innovation

• Collective bargaining for information producers
• Federated data management tools
• Explainable data value estimations