How Do AI Companies "Fine-Tune" Policy? Examining Regulatory Capture in AI Governance

Abstract: Industry actors in the United States have gained extensive influence in conversations about the regulation of general-purpose AI systems. Although industry participation is an important part of the policy process, it can also cause regulatory capture, whereby industry co-opts regulatory regimes to prioritize private over public welfare. Capture of AI policy by AI developers and deployers could hinder such regulatory goals as ensuring the safety, fairness, beneficence, transparency, or innovation of general-purpose AI systems. In this paper, we first introduce different models of regulatory capture from the social science literature. We then present results from interviews with 17 AI policy experts on what policy outcomes could compose regulatory capture in US AI policy, which AI industry actors are influencing the policy process, and whether and how AI industry actors attempt to achieve outcomes of regulatory capture. Experts were primarily concerned with capture leading to a lack of AI regulation, weak regulation, or regulation that over-emphasizes certain policy goals over others. Experts most commonly identified agenda-setting (15 of 17 interviews), advocacy (13), academic capture (10), information management (9), cultural capture through status (7), and media capture (7) as channels for industry influence. To mitigate these particular forms of industry influence, we recommend systemic changes in developing technical expertise in government and civil society, independent funding streams for the AI ecosystem, increased transparency and ethics requirements, greater civil society access to policy, and various procedural safeguards.

• The paper identifies 15 distinct industry influence mechanisms that contribute to regulatory capture in AI governance.
• It employs a structured framework to reveal how AI firms shape policy outcomes through both direct lobbying and subtle agenda-setting tactics.
• The study urges practical measures like enhanced transparency, government capacity-building, and robust ethics reviews to mitigate regulatory capture.

Regulatory Capture in AI Governance: Influence Mechanisms and Mitigation

The study titled "How Do AI Companies 'Fine-Tune' Policy? Examining Regulatory Capture in AI Governance" offers a comprehensive analysis of industry influence within AI policy frameworks. Conducted by Wei et al., the research critically examines how AI companies may affect regulatory outcomes, potentially leading to regulatory capture—a scenario where regulation favors private over public interests.

Regulatory Capture Framework

The authors present a structured framework to understand regulatory capture within AI governance. Regulatory capture is defined explicitly as policy outcomes that contradict public interest goals due to industry influence mechanisms. The paper identifies detrimental effects such as weak AI regulations or the prioritization of industry over public welfare.

Influence Mechanisms and Outcomes

Wei et al. detail 15 distinct mechanisms through which AI industry actors exert influence over policymakers. These mechanisms span from direct actions like advocacy and the revolving door phenomenon to subtler forms such as agenda-setting and academic capture. Notably, agenda-setting emerged as a prevalent form of influence, with industry actors often shaping the narratives and benchmarks for AI policy to align with their interests.

The study highlights that many AI policy outcomes do not yet reflect capture but remain susceptible due to the current levels of industry influence. The predominant concern is weakened regulations rather than the over-regulation model formerly seen in other domains.

Policy Implications and Practical Measures

Wei et al. suggest practical measures to mitigate potential capture, emphasizing building technical capacity within government and promoting transparency. Institutional safeguards, such as enhanced ethics reviews and fostering robust civil society institutions, are suggested to resist undue industry influence. These measures are critical as there is a need to balance necessary industry input in AI policy with a framework that prevents capture.

Speculation on Future Developments

As AI technologies evolve rapidly, the alignment of advanced regulatory frameworks with public interest goals remains a significant challenge. The paper posits additional work to refine interventions that address the diverse models of capture in this dynamic field. By enhancing regulatory capacity and instituting robust participatory frameworks, the potential for AI to serve public interests broader than those of corporate entities increases substantially.

Conclusion

The research by Wei et al. offers a vital foundation for understanding and addressing regulatory capture in AI governance. By identifying the mechanisms through which AI companies could exert influence, the study emphasizes the urgent need for systemic changes that ensure AI policy aligns with societal welfare. As AI continues to integrate into various sectors, ensuring democratic and transparent governance of AI systems is a continual effort that requires vigilance and adaptive policy frameworks.