Varun Nagaraj Rao will present his FPO "Toward Human-Centered, Evidence-Based Policy on AI's Labor Impacts" on Wednesday, May 27, 2026 at 10:00 AM in Sherrerd Hall 306 (CITP Conference Room) and Zoom. Location: Zoom link: https://princeton.zoom.us/j/96896715417?pwd=nls0CXVf7w6pfpbG0QbfnDU8OiDWPi.1&jst=2 The members of Varun’s committee are as follows: Examiners: Andrés Monroy-Hernández (Adviser), Arvind Narayanan, Peter Henderson Readers: Manoel Horta Ribeiro, Solon Barocas (Microsoft Research) A copy of his thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Public discussion of AI and labor often centers on future job loss, obscuring how AI and associated algorithmic systems are transforming work today. For policymakers, labor organizers, and civil society groups, a key challenge is understanding how these systems currently shape workers’ pay, opportunities, and ability to contest decisions, and how to gather evidence for oversight and intervention. However, such evidence is hard to obtain. The systems managing work are often opaque, firm-controlled, and rapidly changing, and workers’ experiences are often embedded in unstructured sources. As a result, workers lack visibility into how these systems operate, and access to the data needed to challenge consequential decisions, and understand their impacts. In this dissertation, I show how worker-centered evidence about these impacts can be generated and help address these information and power asymmetries, in three ways. First, I develop a LLM-based sensemaking method to extract worker concerns from large-scale unstructured text and evaluate whether it can support policy research. Second, I study opaque AI and algorithmic decisions in social media job advertising and rideshare work to understand how they shape pay and opportunity. Third, I develop systems with labor organizations to generate quantitative evidence for legislation and appeals under state policy. Taken together, this dissertation shows how to generate policy-relevant, human-centered evidence about AI’s labor impacts and translate it into forms that inform policy and support workers.