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Characterising Bias in Compressed Models

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arxiv 2010.03058 v2 pith:6IF4IUBB submitted 2020-10-06 cs.LG cs.AI

Sara Hooker , Nyalleng Moorosi , Gregory Clark , Samy Bengio , Emily Denton This is my paper · ORCID
classification cs.LGcs.AI
keywords subsetcompressionexamplesaccuracyauditingbiasdisproportionatelyfurther
verification ladder T0 review T1 audit T2 compute T3 formal
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The popularity and widespread use of pruning and quantization is driven by the severe resource constraints of deploying deep neural networks to environments with strict latency, memory and energy requirements. These techniques achieve high levels of compression with negligible impact on top-line metrics (top-1 and top-5 accuracy). However, overall accuracy hides disproportionately high errors on a small subset of examples; we call this subset Compression Identified Exemplars (CIE). We further establish that for CIE examples, compression amplifies existing algorithmic bias. Pruning disproportionately impacts performance on underrepresented features, which often coincides with considerations of fairness. Given that CIE is a relatively small subset but a great contributor of error in the model, we propose its use as a human-in-the-loop auditing tool to surface a tractable subset of the dataset for further inspection or annotation by a domain expert. We provide qualitative and quantitative support that CIE surfaces the most challenging examples in the data distribution for human-in-the-loop auditing.

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Cited by 5 Pith papers

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    Quantization leaves refusal and multiple-choice bias checks flat while open-ended stereotype endorsement remains high (~24–27% under an independent judge), a gap standard safety evaluations miss.

  2. The Asymmetric Effects of Knowledge Distillation on Bias in Small Language Models

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    Response-based knowledge distillation makes small models less likely to pick stereotypes on unambiguous questions but destroys which specific ambiguous questions they abstain on, a per-item harm aggregate bias metrics hide.

  3. Laplace Sample Information: Data Informativeness Through a Bayesian Lens

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    LSI ranks training samples by informativeness using the KL divergence between Laplace-approximated posteriors with and without each sample, and the ordering transfers from a small probe to larger models.

  4. The Uneven Impact of Post-Training Quantization in Machine Translation

    cs.CL 2025-08 conditional novelty 5.0 of 10

    Across five LLMs and four quantization methods, 4-bit compression mostly preserves translation quality for high-resource languages, while 2-bit compression disproportionately degrades low-resource and Indic languages,...

  5. Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information

    cs.LG 2025-06 reject novelty 4.0 of 10

    A PVI-based data reduction and progressive training strategy is applied to Chinese NLI, but the reported small accuracy declines do not match the experimental tables.

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