Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
This work addresses the need for better evaluation of bias in AI, particularly for multi-class classification, which is an incremental improvement in fairness metrics.
The paper tackles the problem of quantifying bias and fairness in multi-class classifiers by proposing two new metrics, Combined Error Variance (CEV) and Symmetric Distance Error (SDE), and demonstrates their practical application for measuring bias and fairness.
Algorithmic bias is of increasing concern, both to the research community, and society at large. Bias in AI is more abstract and unintuitive than traditional forms of discrimination and can be more difficult to detect and mitigate. A clear gap exists in the current literature on evaluating the relative bias in the performance of multi-class classifiers. In this work, we propose two simple yet effective metrics, Combined Error Variance (CEV) and Symmetric Distance Error (SDE), to quantitatively evaluate the class-wise bias of two models in comparison to one another. By evaluating the performance of these new metrics and by demonstrating their practical application, we show that they can be used to measure fairness as well as bias. These demonstrations show that our metrics can address specific needs for measuring bias in multi-class classification.