Protecting marginalized communities by mitigating discrimination in toxic language detection
Bibliographic record
Abstract
As the harms of online toxic language become more apparent, countering online toxic behavior is an essential application of natural language processing. The first step in managing toxic language risk is identification, but algorithmic approaches have themselves demonstrated bias. Texts containing some demographic identity terms such as gay or Black are more likely to be labeled as toxic in existing toxic language detection datasets. In many machine learning models introduced for toxic language detection, non-toxic comments containing minority and marginalized community-specific identity terms were given unreasonably high toxicity scores. To address the challenge of bias in toxic language detection, we propose a two-step training approach. A pretrained language model with a multitask learning objective will mitigate biases in the toxicity classifier prediction. Experiments demonstrate that jointly training the pretrained language model with a multitask objective can effectively mitigate the impacts of unintended biases and is more robust to model bias towards commonly-attacked identity groups presented in datasets without significantly hurting the model’s generalizability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".