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toxicity detection tools

Toxicity detection tools are automated software applications and machine learning models designed to identify, measure, and flag harmful, abusive, or offensive language in digital text. Commonly utilized in online content moderation, digital communications platforms, and artificial intelligence safety benchmarking, these systems analyze written natural language to detect behaviors such as hate speech, harassment, profanity, and personal attacks. By classifying text into predefined categories of harmful communication or generating numerical scores that reflect the likelihood or severity of inappropriate content, they enable automated filtering, assist human moderators, and facilitate the safety evaluation of conversational agents and foundation models.

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On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research

On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research

Luiza Pozzobon, Beyza Ermis, Patrick Lewis, Sara Hooker

OrganizationsCohereRecod.aiSchool of Electrical and Computer EngineeringUniversity of Campinas

Why you should read this

Reveals how unannounced updates to commercial toxicity evaluation APIs like Perspective alter benchmark leaderboards and invalidate prior research conclusions, while establishing concrete guidelines to ensure reproducible evaluations over time.

Perception of toxicity evolves over time and often differs between geographies and cultural backgrounds. Similarly, black-box commercially available APIs for detecting toxicity, such as the Perspective API, are not static, but frequently retrained to address any unattended weaknesses and biases. We evaluate the implications of these changes on the reproducibility of findings that compare the relative merits of models and methods that aim to curb toxicity. Our findings suggest that research that relied on inherited automatic toxicity scores to compare models and techniques may have resulted in inaccurate findings. Rescoring all models from HELM, a widely respected living benchmark, for toxicity with the recent version of the API led to a different ranking of widely used foundation models. We suggest caution in applying apples-to-apples comparisons between studies and lay recommendations for a more structured approach to evaluating toxicity over time. 1

Added

2026-09-26