Debias-SparseGPT is a post-training pruning method that uses second-order terms over demographically contrasting inputs to mitigate the bias amplification caused by standard SparseGPT.
HOW THIS AFFECTS YOU
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researcherThis method maintains model sparsity while improving representational fairness.
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policyIt provides a technical pathway to deploy compressed models without significantly increasing demographic bias.