# Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning

arXiv: https://arxiv.org/abs/2411.18442 · HTML: https://mlsanity.com/paper/2411.18442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Feature-Level Domain Adaptation](https://mlsanity.com/paper/1512.04829/) (2015)
- [Enhancing Self-Training Methods](https://mlsanity.com/paper/2301.07294/) (2023)
- [DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning](https://mlsanity.com/paper/2409.20126/) (2024)

## Abstract (arXiv)

Selection bias poses a critical challenge for fairness in machine learning, as models trained on data that is less representative of the population might exhibit undesirable behavior for underrepresented profiles. Semi-supervised learning strategies like self-training can mitigate selection bias by incorporating unlabeled data into model training to gain further insight into the distribution of the population. However, conventional self-training seeks to include high-confidence data samples, which may reinforce existing model bias and compromise effectiveness. We propose Metric-DST, a diversity-guided self-training strategy that leverages metric learning and its implicit embedding space to counter confidence-based bias through the inclusion of more diverse samples. Metric-DST learned more robust models in the presence of selection bias for generated and real-world datasets with induced bias, as well as a molecular biology prediction task with intrinsic bias. The Metric-DST learning strategy offers a flexible and widely applicable solution to mitigate selection bias and enhance fairness of machine learning models.