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, Rao shows how worker-centered evidence about these impacts can be generated and help address these information and power asymmetries, in three ways. First, he develops a LLM-based sensemaking method to extract worker concerns from large-scale unstructured text and evaluate whether it can support policy research. Second, he studies opaque AI and algorithmic decisions in social media job advertising and rideshare work to understand how they shape pay and opportunity. Third, he develops 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.
This talk will be recorded and posted to the CITP website, Media Central and the CITP YouTube channel.
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