Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives

Sarah H. Cen, Salil Goyal, Zaynah Javed, Ananya Karthik, Percy Liang, Daniel E. Ho

Keywords: AI audits, less discriminatory alternatives, scaling laws, Pareto frontiers, burden of proof

TL;DR: We derive a closed-form scaling law for a loss-fairness Pareto frontier, which allows an auditor or plaintiff to determine if a less discriminatory alternative exists with minimal compute, data, and model access.

Abstract:

AI audits play a critical role in AI accountability and safety. They are particularly salient in anti-discrimination law. Several areas of anti-discrimination law implicate what is known as the "less discriminatory alternative" (LDA) requirement, under which a protocol is defensible if no less discriminatory model that achieves comparable performance can be found with reasonable effort. Notably, the burden of proving an LDA exists typically falls on the claimant (the party alleging discrimination). This creates a significant hurdle in AI cases, as the claimant would seemingly need to train a less discriminatory yet high-performing model, a task requiring resources and expertise beyond most litigants. Moreover, developers often restrict access to their models and data as trade secrets, hindering replicability.

In this work, we present a procedure enabling claimants to determine if an LDA exists, even when they have limited compute, data, and model access. To illustrate our approach, we focus on the setting in which fairness is given by demographic parity and performance by binary cross-entropy loss. As our main result, we provide a novel closed-form upper bound for the loss-fairness Pareto frontier (PF). This expression is powerful because the claimant can use it to fit the PF in the ''low-resource regime," then extrapolate the PF that applies to the (large) model being contested, all without training a single large model. The expression thus serves as a scaling law for loss-fairness PFs. To use this scaling law, the claimant would require a small subsample of the train/test data. Then, for a given compute budget, the claimant can fit the context-specific PF by training as few as 7 (small) models. We stress test our main result in simulations, finding that our scaling law applies even when the exact conditions of our theory do not hold.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 9493