When decisive facts sit inside proprietary models, platform logs, and protected databases, who can actually prove a claim? This paper maps the asymmetries that block evidence in AI litigation and proposes a three-part test for resolving access disputes.

A guide to how divergent fair use rulings could reshape AI training, licensing regimes, creative labor, unions, and the future of work.

This paper explores how secrecy compounds across the AI supply chain, and the effects this has on the public interest and innovation.

Trade secrecy isn’t just about keeping AI models under wraps—it actively encourages secrecy, stifles competition, and limits innovation.

This paper reviews how privacy regulations in the US and EU may apply to generative image models. The paper explores market and public interest implications of different privacy law interpretations.