The paper delivers a unified framework for fairness in speech technologies by formalizing seven definitions, organizing research into three paradigms, diagnosing pipeline-specific biases, and mapping mitigations to those sources.
What Do Compressed Deep Neural Networks Forget?
6 Pith papers cite this work. Polarity classification is still indexing.
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Wake Vision pipeline produces a 6M-image person detection dataset for TinyML with 2.2% label error, improving model accuracy up to 6.6% over prior VWW benchmark across architectures and subsets.
3-bit quantization induces new stereotypical biases in 6-21% of previously unbiased BBQ items across three LLMs, undetected by perplexity increases under 3%, with models declining in 'unknown' responses by 17.4%.
SalUn uses gradient-based weight saliency to achieve effective machine unlearning of data, classes, or concepts in image classification and generation, narrowing the gap to exact retraining.
Activation-aware pruning preserves perplexity but amplifies bias in LLMs, with 47-59% of previously neutral items developing new stereotypical responses at 70% sparsity.
BISE extracts bias-free subnetworks from conventionally trained models via pruning, enabling debiased operation without retraining or additional data.
citing papers explorer
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Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI
The paper delivers a unified framework for fairness in speech technologies by formalizing seven definitions, organizing research into three paradigms, diagnosing pipeline-specific biases, and mapping mitigations to those sources.
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Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Wake Vision pipeline produces a 6M-image person detection dataset for TinyML with 2.2% label error, improving model accuracy up to 6.6% over prior VWW benchmark across architectures and subsets.
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Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels
3-bit quantization induces new stereotypical biases in 6-21% of previously unbiased BBQ items across three LLMs, undetected by perplexity increases under 3%, with models declining in 'unknown' responses by 17.4%.
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SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
SalUn uses gradient-based weight saliency to achieve effective machine unlearning of data, classes, or concepts in image classification and generation, narrowing the gap to exact retraining.
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Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI
Activation-aware pruning preserves perplexity but amplifies bias in LLMs, with 47-59% of previously neutral items developing new stereotypical responses at 70% sparsity.
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Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models
BISE extracts bias-free subnetworks from conventionally trained models via pruning, enabling debiased operation without retraining or additional data.