Recalibrating classifiers for interpretable abusive content detection
By Bertie Vidgen, Scott A. Hale, S. Staton, T. Melham, Helen Z. Margetts, Ohad Kammar, Marcin Szymczak. 2020
13 citations recorded by the source corpus.
We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the ‘raw’ scores are used) align poorly with human evaluations. This limits their use for understanding the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK’s 2017 general election. A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.
Subfields: Natural Language Processing, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Gavin Abercrombie
- Svetlana Kiritchenko
- Verena Rieser
- Isar Nejadgholi
- Amanda Cercas Curry
- Kathleen C. Fraser
- Zeerak Talat
- Helen Z. Margetts
In AI Ethics, Fairness & Society
- Isar Nejadgholi
- Svetlana Kiritchenko
- Helen Z. Margetts
- Dong Nguyen
- Kathleen C. Fraser
- Gavin Abercrombie
- Verena Rieser
- Janet B. Pierrehumbert
Citations in the corpus
- HateCheck: Functional Tests for Hate Speech Detection Models (2020, background): “The use of humour, for instance, has been highlighted as an important challenge for hate speech research (van Aken et al., 2018; Qian et al., 2018; Vidgen et al., 2020a).”
- ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI (2021, background): “Abusive language detection has received extensive attention for social media, (see e.g. Vidgen et al., 2020a), but far less within the context of conversational systems.”
- Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective (2020, background): “At the present time, the most achievable sense of explanation is to provide the user with a reliability score of models’ predictions (Miok et al., 2020; Vidgen et al., 2020b).”