DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning
This work addresses fairness issues in ML for applications like computer vision and biomedicine, offering a novel mitigation strategy, though it appears incremental as it builds on self-training techniques.
The paper tackled the problem of mitigating selection bias in machine learning for fairer outcomes, particularly addressing unidentified biases in high-dimensional data, and introduced DCAST, which improved robustness across eleven datasets compared to conventional methods.
Fairness in machine learning seeks to mitigate model bias against individuals based on sensitive features such as sex or age, often caused by an uneven representation of the population in the training data due to selection bias. Notably, bias unascribed to sensitive features is challenging to identify and typically goes undiagnosed, despite its prominence in complex high-dimensional data from fields like computer vision and molecular biomedicine. Strategies to mitigate unidentified bias and evaluate mitigation methods are crucially needed, yet remain underexplored. We introduce: (i) Diverse Class-Aware Self-Training (DCAST), model-agnostic mitigation aware of class-specific bias, which promotes sample diversity to counter confirmation bias of conventional self-training while leveraging unlabeled samples for an improved representation of the underlying population; (ii) hierarchy bias, multivariate and class-aware bias induction without prior knowledge. Models learned with DCAST showed improved robustness to hierarchy and other biases across eleven datasets, against conventional self-training and six prominent domain adaptation techniques. Advantage was largest on multi-class classification, emphasizing DCAST as a promising strategy for fairer learning in different contexts.