'Choose your Data Wisely': Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes
Pages 3768 - 3772
Abstract
Data-driven (deep) learning methods has led to parameterised abstractions of the data, often leading to stereotype societal biases in their predictions, e.g., predicting more frequently that women are weaker than men, or that African Americans are more likely to commit crimes than Caucasians. Standard approaches of mitigating such stereotypical biases from deep neural models include modifying the training dataset (pre-processing), or adjusting the model parameters with a bias-specific objective (in-processing). In our work, we approach this bias mitigation from a different perspective - that of an active learning-based selection of a subset of data instances towards training a model optimised for both effectiveness and fairness. Specifically speaking, the imbalances in the attribute value priors can be alleviated by constructing a balanced subset of the data instances with two selection objectives - first, of improving the model confidence of the primary task itself (a standard practice in active learning), and the second, of taking into account the parity of the model predictions with respect to the sensitive attributes, such as gender and race etc. We demonstrate that our proposed selection function achieves better results in terms of both the primary task effectiveness and fairness. The results are further shown to improve when this active learning-based data selection is combined with an in-process method of multi-objective training.
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Index Terms
- 'Choose your Data Wisely': Active Learning based Selection with Multi-Objective Optimisation for Mitigating Stereotypes
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DOI:10.1145/3583780
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