As AI becomes increasingly integrated into both the private and public sectors, challenges around AI safety and policy have arisen. There is a growing, compelling body of work around the legal and societal challenges that come with AI, but there is a gap in our rigorous understanding of these problems. In this talk, I dive deep into a few topics in AI safety and policy. We will discuss AI supply chains (the increasingly complex ecosystem of AI actors and components that contribute to AI products) and study how AI supply chains complicate machine learning objectives. We'll then shift our discussion to AI audits and evidentiary burdens in cases involving AI. Using Pareto frontiers as a tool for assessing performance-fairness tradeoffs, we will show how a closed-form expression for performance-fairness Pareto frontiers can help plaintiffs (or auditors) overcome evidentiary burdens or a lack of access in AI contexts.