Computer Science > Machine Learning
[Submitted on 20 Sep 2023 (v1), last revised 25 Sep 2023 (this version, v2)]
Title:Fairness Hub Technical Briefs: AUC Gap
View PDFAbstract:To measure bias, we encourage teams to consider using AUC Gap: the absolute difference between the highest and lowest test AUC for subgroups (e.g., gender, race, SES, prior knowledge). It is agnostic to the AI/ML algorithm used and it captures the disparity in model performance for any number of subgroups, which enables non-binary fairness assessments such as for intersectional identity groups. The teams use a wide range of AI/ML models in pursuit of a common goal of doubling math achievement in low-income middle schools. Ensuring that the models, which are trained on datasets collected in many different contexts, do not introduce or amplify biases is important for achieving the goal. We offer here a versatile and easy-to-compute measure of model bias for all the teams in order to create a common benchmark and an analytical basis for sharing what strategies have worked for different teams.
Submission history
From: Jinsook Lee [view email][v1] Wed, 20 Sep 2023 19:53:04 UTC (146 KB)
[v2] Mon, 25 Sep 2023 21:04:44 UTC (145 KB)
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