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Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy.
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This content will become publicly available on January 1, 2027
No Free Lunch in Language Model Bias Mitigation? Targeted Bias Reduction Can Exacerbate Unmitigated LLM Biases
Large Language Models (LLMs) inherit societal biases from their training data, potentially leading to harmful outputs. While various techniques aim to mitigate these biases, their effects are typically evaluated only along the targeted dimension, leaving cross-dimensional consequences unexplored. This work provides the first systematic quantification of cross-category spillover effects in LLM bias mitigation. We evaluate four bias mitigation techniques (Logit Steering, Activation Patching, BiasEdit, Prompt Debiasing) across ten models from seven families, measuring impact on racial, religious, profession-, and gender-related biases using the StereoSet benchmark. Across 160 experiments yielding 640 evaluations, we find that targeted interventions cause collateral degradations to model coherence and performance along debiasing objectives in 31.5% of untargeted dimension evaluations. These findings provide empirical evidence that debiasing improvements along one dimension can come at the cost of degradation in others. We introduce a multi-dimensional auditing framework and demonstrate that single-target evaluations mask potentially severe spillover effects, underscoring the need for robust, multi-dimensional evaluation tools when examining and developing bias mitigation strategies to avoid inadvertently shifting or worsening bias along untargeted axes.
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- Award ID(s):
- 2331722
- PAR ID:
- 10678720
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- AI
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2673-2688
- Page Range / eLocation ID:
- 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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