Classification Drives Geographic Bias in Street Scene Segmentation

Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier work studied general-purpose image datasets (e.g., ImageNet) and simple tasks like image recognition, we investigated geo-biases in real-world driving datasets on a more complex task: instance segmentation. We examined if instance segmentation models trained on European driving scenes (Eurocentric models) are geo-biased. Consistent with previous work, we found that Eurocentric models were geo-biased. Interestingly, we found that geo-biases came from classification errors rather than localization errors, with classification errors alone contributing 10-90% of the geo-biases in segmentation and 19-88% of the geo-biases in detection. This showed that while classification is geo-biased, localization (including detection and segmentation) is geographically robust. Our findings show that in region-specific models (e.g., Eurocentric models), geo-biases from classification errors can be significantly mitigated by using coarser classes (e.g., grouping car, bus, and truck as 4-wheeler).

Making Bias Amplification in Balanced Datasets Directional and Interpretable

Most of the ML datasets we use today are biased. When we train models on these biased datasets, they often not only learn dataset biases but can also amplify them -- a phenomenon known as bias amplification. Several co-occurrence-based metrics have been proposed to measure bias amplification between a protected attribute A (e.g., gender) and a task T (e.g., cooking). However, these metrics fail to measure biases when A is balanced with T. To measure bias amplification in balanced datasets, recent work proposed a predictability-based metric called leakage amplification. However, leakage amplification cannot identify the direction in which biases are amplified. In this work, we propose a new predictability-based metric called directional predictability amplification (DPA). DPA measures directional bias amplification, even for balanced datasets. Unlike leakage amplification, DPA is easier to interpret and less sensitive to attacker models (a hyperparameter in predictability-based metrics). Our experiments on tabular and image datasets show that DPA is an effective metric for measuring directional bias amplification. The code will be available soon.