# Prune Responsibly

arXiv: https://arxiv.org/abs/2009.09936 · HTML: https://mlsanity.com/paper/2009.09936/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 20 of 20)

- [Membership Inference Attacks and Defenses in Neural Network Pruning](https://mlsanity.com/paper/2202.03335/) (2022)
- [Pruning has a disparate impact on model accuracy](https://mlsanity.com/paper/2205.13574/) (2022)
- [The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks](https://mlsanity.com/paper/2203.04466/) (2022)
- [Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction](https://mlsanity.com/paper/2110.08232/) (2021)
- [Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging](https://mlsanity.com/paper/2306.16788/) (2023)
- [OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition](https://mlsanity.com/paper/2409.13652/) (2024)
- [Scaling Up Exact Neural Network Compression by ReLU Stability](https://mlsanity.com/paper/2102.07804/) (2021)
- [The Effect of Model Compression on Fairness in Facial Expression Recognition](https://mlsanity.com/paper/2201.01709/) (2022)
- [Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm](https://mlsanity.com/paper/2206.02976/) (2022)
- [Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions](https://mlsanity.com/paper/2301.07966/) (2023)
- [LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging](https://mlsanity.com/paper/2406.12837/) (2024)
- [Review of Demographic Fairness in Face Recognition](https://mlsanity.com/paper/2502.02309/) (2025)
- [Model Compression Techniques in Biometrics Applications: A Survey](https://mlsanity.com/paper/2401.10139/) (2024)
- [A Fair Loss Function for Network Pruning](https://mlsanity.com/paper/2211.10285/) (2022)
- [Curb Your Carbon Emissions: Benchmarking Carbon Emissions in Machine Translation](https://mlsanity.com/paper/2109.12584/) (2021)
- [Integrating Fairness and Model Pruning Through Bi-level Optimization](https://mlsanity.com/paper/2312.10181/) (2023)
- [Efficient DNN-Powered Software with Fair Sparse Models](https://mlsanity.com/paper/2407.02805/) (2024)
- [Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate](https://mlsanity.com/paper/2505.01985/) (2025)
- [Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models](https://mlsanity.com/paper/2603.05582/) (2026)
- [Input-Layer Starvation: Why Per-Layer Pruning Breaks IoT Intrusion Detectors](https://mlsanity.com/paper/2609.30729/) (2026)

## Abstract (arXiv)

Irrespective of the specific definition of fairness in a machine learning application, pruning the underlying model affects it. We investigate and document the emergence and exacerbation of undesirable per-class performance imbalances, across tasks and architectures, for almost one million categories considered across over 100K image classification models that undergo a pruning process.We demonstrate the need for transparent reporting, inclusive of bias, fairness, and inclusion metrics, in real-life engineering decision-making around neural network pruning. In response to the calls for quantitative evaluation of AI models to be population-aware, we present neural network pruning as a tangible application domain where the ways in which accuracy-efficiency trade-offs disproportionately affect underrepresented or outlier groups have historically been overlooked. We provide a simple, Pareto-based framework to insert fairness considerations into value-based operating point selection processes, and to re-evaluate pruning technique choices.