Governing AI Through SEC Disclosure
An analysis of how existing SEC disclosure frameworks can be leveraged to mandate transparency in AI development and deployment, addressing systemic risks through established regulatory mechanisms.
Research
& Publications
Systematic research and policy analysis drawing on large datasets, cutting edge statistical techniques, and inter-disciplinary theories from economics, computer science, and more.
Protocols and Power
Examining how technical protocols and standards in AI infrastructure concentrate power among a small number of corporate actors, and the implications for competitive markets and democratic oversight.
MCP In Practice
A practical examination of Model Context Protocol (MCP) implementations across enterprise AI deployments, analyzing transparency gaps and governance challenges in real-world settings.
The Attribution Crisis: LLM Search Results
Investigating how AI-powered search results fail to properly attribute sources, eroding the information commons and creating accountability gaps in the knowledge economy.
Real-World Gaps in AI Governance Research
A systematic review identifying critical gaps between academic AI governance proposals and their practical implementation in corporate and regulatory contexts.
Beyond Public Access in LLM Pre-Training Data
Documenting evidence of copyrighted O'Reilly Media content appearing in AI training datasets without authorization, with implications for intellectual property rights and creator compensation.