Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI

Lucas Rosenblatt, Lorena Piedras, Julia Wilkins


Abstract
Detecting “toxic” language in internet content is a pressing social and technical challenge. In this work, we focus on Perspective API from Jigsaw, a state-of-the-art tool that promises to score the “toxicity” of text, with a recent model update that claims impressive results (Lees et al., 2022). We seek to challenge certain normative claims about toxic language by proposing a new benchmark, Selected Adversarial SemanticS, or SASS. We evaluate Perspective on SASS, and compare to low-effort alternatives, like zero-shot and few-shot GPT-3 prompt models, in binary classification settings. We find that Perspective exhibits troubling shortcomings across a number of our toxicity categories. SASS provides a new tool for evaluating performance on previously undetected toxic language that avoids common normative pitfalls. Our work leads us to emphasize the importance of questioning assumptions made by tools already in deployment for toxicity detection in order to anticipate and prevent disparate harms.
Anthology ID:
2022.nlp4pi-1.2
Volume:
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Laura Biester, Dorottya Demszky, Zhijing Jin, Mrinmaya Sachan, Joel Tetreault, Steven Wilson, Lu Xiao, Jieyu Zhao
Venue:
NLP4PI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15–24
Language:
URL:
https://aclanthology.org/2022.nlp4pi-1.2
DOI:
10.18653/v1/2022.nlp4pi-1.2
Bibkey:
Cite (ACL):
Lucas Rosenblatt, Lorena Piedras, and Julia Wilkins. 2022. Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI. In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pages 15–24, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI (Rosenblatt et al., NLP4PI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.nlp4pi-1.2.pdf
Video:
 https://aclanthology.org/2022.nlp4pi-1.2.mp4