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Perception of toxicity evolves over time and often differs betweengeographies and cultural backgrounds. Similarly, black-box commerciallyavailable APIs for detecting toxicity, such as the Perspective API, are notstatic, but frequently retrained to address any unattended weaknesses andbiases. We evaluate the implications of these changes on the reproducibility offindings that compare the relative merits of models and methods that aim tocurb toxicity. Our findings suggest that research that relied on inheritedautomatic toxicity scores to compare models and techniques may have resulted ininaccurate findings. Rescoring all models from HELM, a widely respected livingbenchmark, for toxicity with the recent version of the API led to a differentranking of widely used foundation models. We suggest caution in applyingapples-to-apples comparisons between studies and lay recommendations for a morestructured approach to evaluating toxicity over time. Code and data areavailable at https://github.com/for-ai/black-box-api-challenges.

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