Algorithmic Credentialism -- by Peter Q. Blair, Rui Guo

The paper develops a framework for evaluating credential-coded algorithmic screens under existing civil rights law. AI-powered hiring tools trained on historical data often encode and automate bachelor's degree requirements as a proxy for worker skill, producing what this paper terms algorithmic credentialism. Drawing on labor economics research on workers Skilled Through Alternative Routes (STARs), disability theory's critique of the medical model, and disparate-impact doctrine from Griggs v. Duke Power Co. through the Civil Rights Act of 1991, it argues that such screens misidentify the source of hiring exclusion, locating it in the worker rather than in the design of the selection system. This institutional-design problem is amenable to evaluation under the existing disparate-impact doctrine. Under the paper's proposed framework, a credential-coded screen producing disparate impact triggers a legal obligation to validate the screen as job-related and consistent with business necessity, with skills-based hiring supplying the less-discriminatory alternative. The paper further argues that the efficiency rationale historically offered for degree screens is weakened, not strengthened, by AI. The same computational power that automates credentialism at scale can instead match applicants to jobs on multidimensional skill profiles, making the bachelor's degree a less necessary (and less legally defensible) proxy than it was in the pre-AI labor market.