Researchers have introduced Debias-SparseGPT, a post-training pruning framework designed to mitigate the bias amplification often caused by weight sparsification in Large Language Models (LLMs). While existing methods like SparseGPT enable efficient deployment, they frequently exacerbate demographic biases, causing model outputs to shift significantly based on persona-driven prompt cues. To address this, the authors propose a method that integrates representational debiasing into the pruning process by utilizing a second-order term calculated across demographically contrasting input sets. The technique was empirically validated across various generative LLMs using multiple sparsity regimes, including 25%, 50%, and structured 2:4 sparsity patterns. Experimental results demonstrate that Debias-SparseGPT consistently reduces pruning-induced bias compared to standard SparseGPT without sacrificing zero-shot accuracy or model perplexity. Notably, when applying the highly restrictive 2:4 structured sparsity pattern—which typically causes the most significant degradation in model quality—the researchers found that augmenting the calibration set with content-rich, long-context examples further enhances both downstream task performance and fairness metrics. Ultimately, the method improves the fundamental bias-performance trade-off, allowing for the deployment of sparse, computationally efficient models that maintain high levels of fairness and utility.