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Challenges in Automated Debiasing for Toxic Language Detection

Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Noah A. Smith, Yejin Choi

arXiv:2102.00086v1cs.CL

TL;DR

Toxic language detectors can encode lexical and dialectal biases from imperfect training data, motivating evaluation of existing debiasing methods. The paper compares model-based and filtering approaches with dialect-aware relabeling, finding that conventional debiasing remains limited while relabeling reduces dialectal associations with toxicity.

  • Problem

    Existing toxic-language data and detectors contain lexical and dialectal biases, including associations between toxicity and African American English markers.

  • Method

    The paper evaluates debiasing objectives and data-filtering methods, then explores synthetic dialect translation to relabel potentially biased toxic examples.

  • Results

    Existing debiasing methods face significant challenges, whereas dialect-aware relabeling reduces dialectal and racial bias associations in the resulting model.

  • Takeaways & Limitations

    Improving the quality of underlying toxic-language labels may be more effective than relying solely on automatic debiasing for existing imperfect datasets.

  • Takeaways & Limitations

    The synthetic relabeling scheme is illustrative and should not be used in real-world applications because GPT-3 may underrepresent AAE varieties and generate toxic translations.

Abstract

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Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investigate recently introduced debiasing methods for text classification datasets and models, as applied to toxic language detection. Our focus is on lexical (e.g., swear words, slurs, identity mentions) and dialectal markers (specifically African American English). Our comprehensive experiments establish that existing methods are limited in their ability to prevent biased behavior in current toxicity detectors. We then propose an automatic, dialect-aware data correction method, as a proof-of-concept. Despite the use of synthetic labels, this method reduces dialectal associations with toxicity. Overall, our findings show that debiasing a model trained on biased toxic language data is not as effective as simply relabeling the data to remove existing biases.

1 Introduction

Toxic language detectors exhibit discriminatory behavior because biased datasets associate lexical and dialectal surface patterns with toxicity. The paper evaluates debiasing approaches and finds that they struggle to remove these biases, while dialect-aware relabeling appears more promising.

  • Toxic language systems can flag minority identity mentions while missing comparable majority mentions, creating disparate negative impacts on minority populations.
  • Lexical bias associates toxicity with profanities and identity mentions, while dialectal bias associates it with African American English markers.
  • The study investigates debiasing methods using additional bias-removal objectives and filtering of training instances with likely spurious biases.
  • Both approaches face major challenges: filtered-data models still acquire lexical and dialectal biases, and debiased models disproportionately flag some dialects as toxic.
  • A synthetic dialect-translation relabeling method produces models less prone to dialectal and racial biases, although the authors present it only as an illustrative proof of concept.

2 Biases in Toxic Language Detection

The paper distinguishes lexical and dialectal biases in toxic-language datasets and examines them in a widely used English-tweet dataset. These biases are socially grounded and can cause detectors to associate toxicity with identity-related words or African American English.

  • Toxic language detection aims to flag rude, offensive, hateful, or toxic internet language for moderating online communities.
  • The paper defines its focus as lexical and dialectal social biases and studies them primarily in one widely used dataset from Founta et al. (2018).
  • Lexical Biases (TOXTRIG): TOXTRIG is the full set of manually categorized words whose profane, hateful, or context-associated meanings can influence toxicity predictions.
  • Lexical Biases (TOXTRIG): Non-offensive minority identity mentions can occur in hateful contexts, whereas possibly offensive mentions depend on pragmatic and contextual interpretation.
  • Dialectal Biases (AAE): African American English markers are associated with higher toxicity, which can suppress speech by Black authors more often than speech by non-Black authors.
  • Dialectal Biases (AAE): The experiments estimate AAE and white-aligned English using a topic model trained on 60M tweets, then analyze a dataset of 86k labeled tweets.

3 Debiasing Methods

The paper compares model-based debiasing for predefined lexical and dialectal biases with data-filtering methods designed to remove examples containing unspecified spurious correlations. These approaches rely on bias-specific models, filtered subsets, and RoBERTa-large classifiers, but their applicability is constrained by dataset coverage and collection artifacts.

  • The study evaluates model-based debiasing with additional objectives and bias-agnostic filtering of examples that may contain spurious correlations.
  • LEARNED-MIXIN combines a bias-only model using predefined bias features with a full model, then discards the bias-only model for inference.
  • The Davidson et al. dataset produced degenerate debiasing behavior because its collection process used a seed list of swear words and slurs.
  • The bias-only models use lexical or dialect probabilities, while the full model is a fine-tuned RoBERTa-large toxicity classifier.
  • AFLite iteratively discards examples that simple classifier ensembles predict correctly, treating these predictable instances as likely carriers of spurious biases.
  • DataMaps partitions examples into easy, hard, and ambiguous regions using model confidence and confidence variability across training epochs.
  • The study filters both datasets to 33% of the original training size while preserving original label proportions, then fine-tunes RoBERTa-large on each subset.

4 Experiments: Lexical Biases

The experiments evaluate lexical debiasing through bias-sensitive metrics, filtered datasets, debiased training, and adversarial testing. Results show trade-offs between reducing lexical associations, preserving performance, and recognizing toxicity without overt lexical cues.

  • Evaluation framework: The evaluation measures overall accuracy and toxic-class F1 alongside false positive rates for tweets containing NOI, OI, and ONI mentions.Lower FPR indicates weaker inferred lexical associations with toxicity.
  • Filtered datasets: Lower Pearson R correlations in filtered datasets indicate fewer associations between TOXTRIG mentions and toxicity; DataMaps-Hard produces the largest reduction.AFLite and DataMaps-Ambiguous also reduce associations, whereas DataMaps-Easy increases them.
  • Debiased training: LMIXIN-ONI and LMIXIN-TOXTRIG substantially reduce FPRONI and FPROI but hurt in-distribution performance.The results suggest ONI and other TOXTRIG features contribute to good in-distribution performance.
  • Filtered-model results: DataMaps-Hard and DataMaps-Ambiguous preserve in-distribution performance while training on only 33% of the original data.They reduce FPRNOI but show limited improvement for ONI and retain the original baseline’s FPROI.
  • Filtered-model results: DataMaps-Easy reduces bias on NOI and ONI despite stronger dataset-level lexical associations, but its F1 deteriorates across categories.No single approach performs best across all lexical-bias categories.
  • Qualitative analysis: Qualitative analysis identifies annotation errors and ambiguities that may impair measurements of model performance and debiasing effects.Examples include atypical toxicity, reliance on a partially obscured profanity, and violence directed at an atypical identity target.
  • Adversarial evaluation: On ONI-Adv, debiased training improves F1 over baselines, whereas data filtering does not, and none of the tested models reliably detects subtle non-overt toxicity.ONI-Adv contains 3,000 manually created sentences, including 300 toxic examples with few ONI mentions.

5 Experiments: Dialectal and Racial Biases

Dialectal debiasing methods generally reduce measured dialectal bias, but these gains do not consistently transfer to racial disparities in toxicity flagging on out-of-distribution tweets.

  • Dialectal Biases: Dialectal bias was measured using toxicity–AAE correlation and false-positive rates on AAE tweets.The evaluation also reports F1, with lower FPRAAE indicating less bias.
  • Dialectal Biases: Almost all filtering and debiasing methods reduced dialectal bias, except DataMaps-Easy.DataMaps-Hard performed best on both toxicity–AAE correlation and AAE false-positive rate, although most reductions in false flagging were small.
  • Racial Biases: On a 12k-tweet out-of-distribution sample, racial bias was measured as the difference between toxicity-flagging rates for African American and white authors.The analysis uses a 5.4M-tweet corpus collected from 4,132 survey participants and assumes equal likelihood of toxic language across races.
  • Racial Biases: Automatic debiasing did not consistently reduce racial disparities, and DataMaps-Hard combined strong dialectal debiasing with high racial discrepancy.DataMaps-Easy showed the most racial bias in toxicity flagging, while toxicity rates and race differences were otherwise similar across methods and baselines.
  • Dialectal Biases: The dialectal-bias evaluation compares full-data debiased training with 33%-data filtering methods and includes an AAE-relabeling condition.Results report F1 and FPRAAE means with standard deviations across three runs.

6 Towards Data Relabeling

The paper explores dialect-aware relabeling as a response to biased toxicity annotations. A GPT-3-based AAE-to-WAE translation heuristic changes selected labels and reduces dialectal and racial bias, but the synthetic procedure is explicitly limited and not recommended for real-world use.

  • Towards Data Relabeling: The relabeling study treats toxic AAE tweets whose WAE versions are predicted non-toxic as candidates for label correction.This relies on the assumption that an AAE tweet and its WAE counterpart should share a toxicity label.
  • Towards Data Relabeling: The proof of concept uses GPT-3 few-shot prompts to generate WAE translations because qualified gold translations are unavailable.The authors explicitly do not recommend this mechanism for constructing large-scale dialect parallel data.
  • Towards Data Relabeling: 954 of 8,260 toxic AAE tweets, or 12%, were relabeled when either classifier predicted their WAE translation as non-toxic.The resulting AAE-relabeled dataset retained the original dataset size.
  • Towards Data Relabeling: The key assumption does not apply to lexical substitutions because replacing identity terms can change a sentence’s denotational meaning.The study therefore distinguishes dialect-preserving relabeling from lexical-item substitutions.
  • Towards Data Relabeling: Manual review agreed with 84% of the relabeling decisions, while the relabeled model achieved the largest dialectal-bias reduction and the lowest racial disparity.The dialectal improvement came with a one-point F1 decrease, and the gold-test F1 was considered unreliable because the test data contain label biases.
  • Towards Data Relabeling: Relabeling was more effective than debiasing methods at mitigating dialectal dataset bias in the reported experiments.The authors recommend human-written AAE–WAE pairs and involving AAE speakers or experts in toxicity labeling.
  • Towards Data Relabeling: The synthetic translation setting is not recommended for real-world use because GPT-3 may underrepresent AAE varieties and may generate toxic translations.These factors could introduce differential toxicity into the translations.

7 Related Work

Related work documents bias-mitigation efforts in toxic-language detection and broader NLU debiasing. These approaches include data rebalancing, adversarial learning, confidence regularization, and product-of-experts methods.

  • Related Work: The paper situates its contribution within prior toxic-language debiasing research and broader NLU debiasing methods.The related-work discussion distinguishes task-specific efforts from general methods developed for NLU.
  • Related Work: Table 6 illustrates how GPT-3-based WAE translations support new AAE-relabeled annotations alongside original gold labels.The first three examples change biased gold labels using model predictions on translated tweets.
  • Related Work: Toxic-language detection has prompted debiasing efforts targeting identity, gender, racial, and dialectal biases in datasets and models.Examples include data rebalancing and adversarial feature learning, while racial and dialectal bias have received comparatively less attention.
  • Related Work: General NLU debiasing methods include adversarial training, confidence regularization, and product-of-experts approaches.The paper uses the Clark et al. debiased-training approach because of its relatively strong performance.

8 Conclusion

The paper finds that existing debiasing methods face significant challenges on lexical and dialectal biases in toxic language detection, while correcting noisy labels shows better bias reduction. These findings support prioritizing the quality of underlying toxic-language data.

  • Existing debiasing methods face significant challenges in reducing biased behavior in toxicity detectors.
  • Correcting noisy labels results in better bias reduction than relying solely on automatic debiasing for existing imperfect datasets.
  • The findings suggest prioritizing underlying data quality, including speaker identity and dialect, in future toxic-language detection work.
  • The LEARNED-MIXIN ensemble lets the model determine how much to trust a bias-only model for each input.
  • An entropy penalty is added to prevent the LEARNED-MIXIN ensemble from ignoring the bias-only model.

A.2 Data Filtering

The data-filtering setup downsamples training data while preserving the original toxic ratio, and evaluates whether joint predictions maintain in-distribution performance. The joint-prediction models achieve competitive accuracy.

  • Data filtering first retains 50% of the original data, then downsamples to 33% while controlling for the original toxic ratio.The additional downsampling avoids confounding results with different toxic ratios among training sets.
  • All experiments fine-tune RoBERTa-large with a learning rate of 10^-5 and batch size 8.
  • Joint predictions from the main and bias-only models are used to infer the in-distribution test set.
  • 94.15% and 94.17% accuracy are obtained by the two joint-prediction models, respectively, with competitive performance reported in Table 2.

B Alternative Dataset of Toxic Language

On the Davidson et al. dataset, data-filtering methods produce degenerate behavior because the easy region has a heavily skewed class distribution that prevents toxic-ratio-controlled downsampling.

  • The Davidson et al. dataset was collected from Twitter using 1,000 HateBase terms as lexical-bias seeds.
  • Filtering this dataset leads to degenerate behavior because the easy region has the least spurious correlation and a heavily skewed class distribution.
  • The skewed easy-region distribution prevents downsampling that controls the toxic ratio.

C Few-shot AAE-to-WAE Translation

The paper describes a few-shot GPT-3 system for translating AAE tweets into WAE, while cautioning against using it to build large-scale dialect parallel data. The surrounding evaluations compare lexical and dialectal associations across debiasing methods and filtered datasets.

  • C Few-shot AAE-to-WAE Translation: The authors do not recommend this approach for building large-scale parallel dialect data.
  • C Few-shot AAE-to-WAE Translation: The proposed approach uses GPT-3 to create a few-shot AAE-to-WAE translation system from example pairs drawn from Spears (1998).
  • C Few-shot AAE-to-WAE Translation: The examples replace or remove some AAE lexical forms, such as “triflin’ ass” to “trifling self” and “his ass” to “him.”
  • C Few-shot AAE-to-WAE Translation: The system prepends formatted example pairs to each AAE training tweet and generates translations with top-0.95 nucleus sampling and temperature 0.5.
  • Evaluation: The evaluation tables compare lexical associations, model performance and lexical-bias false-positive rates, plus dialectal false-positive rates across in-distribution and out-of-distribution tests.
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