# Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation

arXiv: https://arxiv.org/abs/2106.07849 · HTML: https://mlsanity.com/paper/2106.07849/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Similarity-Preserving Knowledge Distillation](https://mlsanity.com/paper/1907.09682/) (2019)
- [Rigging the Lottery: Making All Tickets Winners](https://mlsanity.com/paper/1911.11134/) (2019)
- [Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation](https://mlsanity.com/paper/1911.11834/) (2019)
- [Learning Deep Representations with Probabilistic Knowledge Transfer](https://mlsanity.com/paper/1803.10837/) (2018)
- [What Do Compressed Deep Neural Networks Forget?](https://mlsanity.com/paper/1911.05248/) (2019)
- [Characterising Bias in Compressed Models](https://mlsanity.com/paper/2010.03058/) (2020)
- [Sparse DNNs with Improved Adversarial Robustness](https://mlsanity.com/paper/1810.09619/) (2018)
- [Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation](https://mlsanity.com/paper/2009.13270/) (2020)

## Built on by (most cited first, 15 of 15)

- [Large Language Model Safety: A Holistic Survey](https://mlsanity.com/paper/2412.17686/) (2024)
- [Pruning has a disparate impact on model accuracy](https://mlsanity.com/paper/2205.13574/) (2022)
- [Pruning's Effect on Generalization Through the Lens of Training and Regularization](https://mlsanity.com/paper/2210.13738/) (2022)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures](https://mlsanity.com/paper/2304.12622/) (2023)
- [Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm](https://mlsanity.com/paper/2206.02976/) (2022)
- [Debiased Distillation by Transplanting the Last Layer](https://mlsanity.com/paper/2302.11187/) (2023)
- [Model Compression Techniques in Biometrics Applications: A Survey](https://mlsanity.com/paper/2401.10139/) (2024)
- [Disparate Impact on Group Accuracy of Linearization for Private Inference](https://mlsanity.com/paper/2402.03629/) (2024)
- [Debiasing surgeon: fantastic weights and how to find them](https://mlsanity.com/paper/2403.14200/) (2024)
- [MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition](https://mlsanity.com/paper/2408.16563/) (2024)
- [Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis](https://mlsanity.com/paper/2304.07461/) (2023)
- [Efficient DNN-Powered Software with Fair Sparse Models](https://mlsanity.com/paper/2407.02805/) (2024)
- [Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems](https://mlsanity.com/paper/2408.06102/) (2024)
- [Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models](https://mlsanity.com/paper/2603.05582/) (2026)

## Abstract (arXiv)

In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness. Compared to traditional forms of bias or discrimination caused by humans, algorithmic bias generated by AI is more abstract and unintuitive therefore more difficult to explain and mitigate. A clear gap exists in the current literature on evaluating and mitigating bias in pruned neural networks. In this work, we strive to tackle the challenging issues of evaluating, mitigating, and explaining induced bias in pruned neural networks. Our paper makes three contributions. First, we propose two simple yet effective metrics, Combined Error Variance (CEV) and Symmetric Distance Error (SDE), to quantitatively evaluate the induced bias prevention quality of pruned models. Second, we demonstrate that knowledge distillation can mitigate induced bias in pruned neural networks, even with unbalanced datasets. Third, we reveal that model similarity has strong correlations with pruning induced bias, which provides a powerful method to explain why bias occurs in pruned neural networks. Our code is available at https://github.com/codestar12/pruning-distilation-bias