This work presents a comprehensive evaluation of how quantization affects model bias, with particular attention to its impact on individual demographic subgroups. We focus on weight and activation quantization strategies and examine their effects across a broad range of bias types, including stereotypes, fairness, toxicity, and sentiment. We employ both probability- and generated text-based metrics across 13 benchmarks and evaluate models that differ in architecture family and reasoning ability. Our findings show that quantization has a nuanced impact on bias: while it can reduce model toxicity and does not significantly impact sentiment, it tends to slightly increase stereotypes and unfairness in generative tasks, especially under aggressive compression. These trends are generally consistent across demographic categories and subgroups, and model types, although their magnitude depends on the specific setting. Overall, our results highlight the importance of carefully balancing efficiency and ethical considerations when applying quantization in practice.

翻译：本研究全面评估了量化如何影响模型偏见，特别关注其对个体人口统计子群体的影响。我们重点研究权重和激活量化策略，并考察其对广泛偏见类型（包括刻板印象、公平性、毒性和情感倾向）的影响。我们在13个基准测试中采用基于概率和生成文本的度量方法，评估了不同架构家族和推理能力的模型。我们的研究结果表明，量化对偏见的影响具有细微差别：虽然它可以降低模型毒性且不会显著影响情感倾向，但往往会略微增加生成任务中的刻板印象和不公平性，尤其是在激进压缩条件下。这些趋势在不同人口统计类别、子群体以及模型类型中总体一致，尽管其程度取决于具体设置。总体而言，我们的结果强调了在实践中应用量化时，需要仔细权衡效率与伦理考量。