Measuring Mechanistic Independence: Can Bias Be Removed Without Erasing Demographics?

• URL: http://arxiv.org/abs/2512.20796v1
• Date: Tue, 23 Dec 2025 21:44:20 GMT
• Title: Measuring Mechanistic Independence: Can Bias Be Removed Without Erasing Demographics?
• Authors: Zhengyang Shan, Aaron Mueller,
• Abstract summary: We investigate how independent demographic bias mechanisms are from general demographic recognition in language models.<n>We find that attribution-based ablations mitigate race and gender profession stereotypes while preserving name recognition accuracy.<n>We find that correlation-based ablations are more effective for education bias.
• Score: 17.978167351646288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate how independent demographic bias mechanisms are from general demographic recognition in language models. Using a multi-task evaluation setup where demographics are associated with names, professions, and education levels, we measure whether models can be debiased while preserving demographic detection capabilities. We compare attribution-based and correlation-based methods for locating bias features. We find that targeted sparse autoencoder feature ablations in Gemma-2-9B reduce bias without degrading recognition performance: attribution-based ablations mitigate race and gender profession stereotypes while preserving name recognition accuracy, whereas correlation-based ablations are more effective for education bias. Qualitative analysis further reveals that removing attribution features in education tasks induces ``prior collapse'', thus increasing overall bias. This highlights the need for dimension-specific interventions. Overall, our results show that demographic bias arises from task-specific mechanisms rather than absolute demographic markers, and that mechanistic inference-time interventions can enable surgical debiasing without compromising core model capabilities.

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