Scoring hostile language
Toxicity detection classifies text for rudeness, insults, threats, or hate, usually producing a probability per category. It feeds moderation, dataset cleaning, and detoxified generation.
How detectors are built
- Train a classifier on text labeled by humans as toxic or not, often per category.
- Modern detectors use transformer encoders fine tuned on these labels.
- Outputs are thresholded to flag or block content.
Why it is hard
- Context matters: the same word can be a slur, a quote, a reclaimed term, or neutral.
- Sarcasm, dialect, and coded language evade simple detectors.
- Labels are subjective, so different annotators disagree on borderline cases.
Fairness pitfalls
- Detectors often over flag dialects like African American English and mentions of identity groups, because those terms correlate with toxicity in training data.
- This causes biased false positives that silence the very groups moderation should protect.
- Auditing per group error rates is essential.
Key idea
Toxicity detectors score text for hostility but struggle with context, sarcasm, and dialect, and can over flag identity terms, so per group error auditing is essential to avoid biased moderation.