Call for Papers

This workshop invites participants to convene and contribute to knowledge exchange on how private governance mechanisms may work in tandem with government regulation, the type of technical work needed to operationalise such mechanisms, and how technological safety advances and private governance mechanisms may influence and feed into each other.

This workshop thus aims to bring together researchers from technical and policy backgrounds to share insights on how their disciplines can cooperate to achieve effective private governance mechanisms that can contribute to societally beneficial outcomes for AI.

We welcome two types of submissions: 

A. Governance architecture and market design We welcome less-technical submissions addressing concerns, or advocating for positions, relating to the field of private AI governance as a whole. Such submissions could, for example, aim to situate private AI governance within wider AI capabilities, AI regulation, technical AI governance (TAIG) or AI safety discussions. 

Examples (illustrative, not exhaustive) of work relating to governance themes:

• Innovating Liability: The Virtuous Cycle of Torts, Technology and Liability Insurance (Lior, 2023).

• Insuring Emerging Risks from AI (Weil et al., 2024).

• AI Governance through Markets (Tomei et al., 2025).

• Procurement as AI Governance (Ben Dor & Coglianese, 2021).

• Regulatory Markets: The Future of AI Governance (Hadfield & Clark, 2023).

• Insuring Generative AI: Risks and Mitigation Strategies (Munich Re, 2024). 

• Public vs Private Bodies: Who Should Run Advanced AI Evaluations and Audits? A Three-Step Logic Based on Case Studies of High-Risk Industries (Stein et al., 2024).

• A Framework for the Private Governance of Frontier Artificial Intelligence (Ball, 2025).

• Understanding accountability in algorithmic supply chains (Cobbe, Veale & Singh, 2023).

B. Technical artefacts for private oversight We welcome research on tools and approaches that might enable private governance mechanisms and that integrate into assurance, certification, insurance and procurement workflows, as well as perspectives reflecting on their limitations and implications for practice.

Examples (illustrative, not exhaustive) of work relating to technical themes: 

• Holistic Evaluation of Language Models (HELM) (Liang et al., 2022). 

• HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal (Mazeika et al., 2024). 

• Model Cards for Model Reporting (Mitchell et al., 2019). 

• Datasheets for Datasets (Gebru et al., 2021). 

• A Watermark for Large Language Models (Kirchenbauer et al., 2023). 

• C2PA Technical Specification (Content Provenance & Authenticity) (Coalition for Content Provenance and Authenticity (C2PA), 2023). 

• Data Shapley: Equitable Valuation of Data for Machine Learning (Ghorbani & Zou, 2019).

• Estimating Training Data Influence by Tracing Gradient Descent (TracIn) (Pruthi et al., 2020). 

• Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback (Lindstrom et al, 2025). 

• From policy to practice in data governance and responsible data stewardship: System design for data intermediaries (Powar et al, 2025).