Debiasing Static Embeddings for Hate Speech Detection (2025)

Abstract: We examine how embedding bias affects hate speech detection by evaluating two debiasing methods—hard-debiasing and soft-debiasing. We analyze stereotype and sentiment associations within the embedding space and assess whether debiased models reduce censorship of marginalized authors while improving detection of hate speech targeting these groups. Our findings highlight how embedding bias propagates into downstream tasks and demonstrates how well different embedding bias metrics can predict bias in hate speech detection.

## introduction bias in hate speech detection is known to arise from data sources, sampling methods, and pre-trained word embeddings. these different biases distort model predictions, potentially unintentionally linking non-discriminatory terms to hate speech. for instance, @xcite showed that domainrestricted sampling methods statistically induce bias, such as the word commentator becoming indicative of hate speech because of the content domain soccer. beyond dataset biases, pre-trained word embeddings can encode and amplify historical and social biases from large-scale text data. @xcite showed that embeddings reinforce stereotypes, such as aligning man with scientist and woman with homemaker. similarly, @xcite reported that identity-related terms, such as african american names, are more strongly associated with negative sentiment than european american names. such biases undermine hate speech detection by (1) damaging model performance in realistic settings where speech does not conform to learned biases @xcite , (2) disproportionately flagging non-hate posts by marginalized groups, reinforcing discrimination, and (3) failing to recognize harmful stereotypes, leading to missed detection of implicit hate against marginalized groups. we will refer to the disproportionate flagging of non-hate posts by marginalized groups as author bias; and we will refer to the failure to recognize harmful stereotypes as target bias. while many studies have addressed biases introduced by datasets and sampling strategies @xcite @xcite , the impact of pre-trained word embeddings on hate speech detection remains underexplored. not only can the pre-trained embeddings encode inaccurate connotations, they can also reinforce stereotypes which are crucial for detecting implicit hate. furthermore, @xcite demonstrated that common debiasing methods for embeddings can introduce new biases and mitigating negative connotation bias may inadvertently reinforce stereotypes. they argue that evaluating debiasing techniques requires assessing the impact on both embedding space associations and downstream task performance. however, in hate speech detection, this dual evaluation remains largely unaddressed. in this study, we examine two popular debiasing methods, hard-debiasing and soft-debiasing, in the context of hate speech detection. we analyze how these methods alter sentiment and stereotype associations of identity terms within the embedding space, and we evaluate whether debiased models exhibit less bias in hate speech classification. specifically, we test whether models disproportionately censor authors from marginalized groups and whether they fail to detect hate speech targeting these groups. 1 our study shows how embedding bias propagates into downstream consequences and evaluates the effectiveness of different embedding bias metrics in predicting bias in hate speech detection. offensive content warning: this report contains some examples of hateful content. this is strictly for the purposes of enabling this research, and we have sought to minimize the number of examples where possible. please be aware that this content could be offensive and cause you distress. ## discussion the inconsistency in debiasing effectiveness across different conditions in the embedding space analysis (section 4.1) aligns with our findings wrt. model bias (section 4.2). for example, soft debiased skipgrams reinforce more gender than racial stereotypes and correspondingly increase the likelihood of missing hate speech against gender but not race. given this, we examine the embedding space to investigate whether it provides a more coherent explanation for these distinctions (section 5.1). we also perform an error analysis to illustrate the effect of embedding bias on hate speech detection (section 5.2). ## conclusion and future work this study explored the role of embedding bias and its impact on hate speech detection bias. our findings highlight several critical conclusions: bias as author vs. target: bias against minority groups differs when they are the authors versus the targets of hate speech. embedding debiasing methods cannot effectively reduce author bias but show limited success for minority target groups. distinct nature of gender and race bias: the distinction between stereotype and sentiment metrics in predicting model bias for identity categories highlights that gender and race bias have fundamentally different characteristics, meaning approaches effective for one cannot be directly applied to the other: (1) mac and ba effectively identify racial bias reductions for minority target groups, particularly asians. (2) rnsa performs better for detecting gender bias improvements. in conclusion, this work underscores the complexity of bias in hate speech detection. bias varies across gender, race, and specific racial groups, necessitating group-specific approaches and nuanced bias metrics. a single debiasing method or metric cannot universally address all bias issues, promoting the need for targeted solutions and refined evaluation frameworks to achieve fairer hate speech detection systems. we will continue our efforts to explore other model architectures, including svms (for explainability) and transformer-based models. additionally, we will investigate debiasing methods for contextualized word embeddings (e.g., @xcite ; @xcite ), which can capture richer semantic and syntactic nuances, to determine whether we see the same interactions between debiasing and evaluation metrics.