  While deep learning has become a core functional module of most software
systems, concerns regarding the fairness of ML predictions have emerged as a
significant issue that affects prediction results due to discrimination.
Intersectional bias, which disproportionately affects members of subgroups, is
a prime example of this. For instance, a machine learning model might exhibit
bias against darker-skinned women, while not showing bias against individuals
with darker skin or women. This problem calls for effective fairness testing
before the deployment of such deep learning models in real-world scenarios.
However, research into detecting such bias is currently limited compared to
research on individual and group fairness. Existing tools to investigate
intersectional bias lack important features such as support for multiple
fairness metrics, fast and efficient computation, and user-friendly
interpretation. This paper introduces Fairpriori, a novel biased subgroup
discovery method, which aims to address these limitations. Fairpriori
incorporates the frequent itemset generation algorithm to facilitate effective
and efficient investigation of intersectional bias by producing fast fairness
metric calculations on subgroups of a dataset. Through comparison with the
state-of-the-art methods (e.g., Themis, FairFictPlay, and TestSGD) under
similar conditions, Fairpriori demonstrates superior effectiveness and
efficiency when identifying intersectional bias. Specifically, Fairpriori is
easier to use and interpret, supports a wider range of use cases by
accommodating multiple fairness metrics, and exhibits higher efficiency in
computing fairness metrics. These findings showcase Fairpriori's potential for
effectively uncovering subgroups affected by intersectional bias, supported by
its open-source tooling at https://anonymous.4open.science/r/Fairpriori-0320.
