Abstract
Machine Learning can help overcome human biases in decision making by focussing on purely logical conclusions based on the training data. If the training data is biased, however, that bias will be transferred to the model and remains undetected as the performance is validated on a test set drawn from the same biased distribution. Existing strategies for selection bias identification and mitigation generally rely on some sort of knowledge of the bias or the ground-truth. An exception is the Imitate algorithm that assumes no knowledge but comes with a strong limitation: It can only model datasets with one normally distributed cluster per class. In this paper, we introduce a novel algorithm, Mimic, which uses Imitate as a building block but relaxes this limitation. By allowing mixtures of multivariate Gaussians, our technique is able to model multi-cluster datasets and provide solutions for a substantially wider set of problems. Experiments confirm that Mimic not only identifies potential biases in multi-cluster datasets which can be corrected early on but also improves classifier performance.
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Notes
- 1.
Implementation and Supplementary Material: https://github.com/KatDost/Mimic.
- 2.
The Central Limit Theorem states that a sequence of independent and identically distributed (i.i.d.) random variables converges almost surely to a Gaussian [10]. Since we can typically assume that real-world measurements are not perfectly i.i.d. but rather combinations of different effects, we will often observe this effect.
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Dost, K., Duncanson, H., Ziogas, I., Riddle, P., Wicker, J. (2022). Divide and Imitate: Multi-cluster Identification and Mitigation of Selection Bias. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13281. Springer, Cham. https://doi.org/10.1007/978-3-031-05936-0_12
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