FairPair: A Robust Evaluation of Biases in Language Models through Paired Perturbations
This work addresses the need for robust and extendable bias evaluation in language models to ensure fair user experiences, though it is incremental as it builds on existing counterfactual methods.
The authors tackled the problem of evaluating biases in language models by introducing FairPair, a framework that uses paired perturbations to assess differential treatment during ordinary usage, and found a preference for discussing family and hobbies with regard to women in several generative models.
The accurate evaluation of differential treatment in language models to specific groups is critical to ensuring a positive and safe user experience. An ideal evaluation should have the properties of being robust, extendable to new groups or attributes, and being able to capture biases that appear in typical usage (rather than just extreme, rare cases). Relatedly, bias evaluation should surface not only egregious biases but also ones that are subtle and commonplace, such as a likelihood for talking about appearances with regard to women. We present FairPair, an evaluation framework for assessing differential treatment that occurs during ordinary usage. FairPair operates through counterfactual pairs, but crucially, the paired continuations are grounded in the same demographic group, which ensures equivalent comparison. Additionally, unlike prior work, our method factors in the inherent variability that comes from the generation process itself by measuring the sampling variability. We present an evaluation of several commonly used generative models and a qualitative analysis that indicates a preference for discussing family and hobbies with regard to women.