S em E val-2023 Task 10: Explainable Detection of Online Sexism (2023)

Abstract: Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.

## introduction online sexism can inflict harm on women who are targeted, make online spaces inaccessible and unwelcoming, and perpetuate social asymmetries and injustices. automated tools are now widely deployed to find sexist content at scale, supporting content moderation, monitoring and research. however, most automated classifiers do not give further explanations beyond generic, high-level categories such as 'toxicity', 'abuse' or 'sexism'. opaque classification without explanations can cause confusion, anger and mistrust among moderated users, and make it harder to challenge moderation mistakes; moderators who use automated tools may not trust or fully understand the labels, reducing their efficiency; and it is difficult to assess model weaknesses in the absence of granular labels, which hinders the development of better models. we organised the semeval 2023 task explainable detection of online sexism (edos), which * equal contribution follows from success of previous shared tasks on abuse and hate detection @xcite @xcite @xcite . our task proposes and applies a taxonomy with three hierarchical tasks for detecting sexist content. task a is a binary task to detect whether content is sexist. for sexist content, task b distinguishes between four distinct categories of sexism, and task c identifies one of 11 fine-grained sexism vectors. there are three factors that make our edos task unique: first, data diversity: we sample data from two large social media platforms (reddit and gab) using an ensemble of filtering methods. the entries are more diverse than sampling from keywords alone or a single platform. second, annotation quality: we recruit highly-trained annotators who all self-identify as women. we make this decision to mitigate implicit biases in labelling and to evoke a participatory approach to ai where the communities primarily harmed from specific forms of content are included in the data development process @xcite . to improve consistency of labels with our detailed guidelines, all annotators were trained in multiple pilot tasks, and disagreements resolved by experts. third, task granularity: we ground our dataset in a carefully-constructed taxonomy such that the multiple categories in tasks b and c are both meaningful from a social scientific perspective, aid explainability in predictions and present a challenging machine learning task for multi-class classification. in releasing the dataset and analysing the accompanying semeval task submissions, we contribute to efforts in building automated sexism detection systems which are both more accurate and explainable via fine-grained labels. 1 these contributions mark a step towards a safer internet for women. ## error analyses we base our error analyses on the 10 best performing systems for each task as their errors indicate the remaining difficulty within our dataset. ## conclusion in this paper, we made three main contributions. first, we provided a new taxonomy for the more explainable classification of sexism in three hierarchical levels -binary sexism detection, category of sexism and fine-grained vector of sexism. this taxonomy is grounded in reviews of prior taxonomies and social science literature, then empirically-validated with in-domain data from two social media platforms. it thus provides a sociotechnical overview of the varied and nuanced landscape of online sexism. second, we created a high-quality dataset, sampled with an ensemble of techniques to increase the diversity of content and annotated by self-identifying women experts to ensure consistent labels. this labelled dataset is paired with a larger unlabelled dataset to mitigate the constraints that a labelling budget (both in terms of financial cost and psychological cost to annotators) has on the efficacy of trained systems. finally, we shared baseline models and summarised systems submitted to our semeval task. this analysis demonstrates the success of techniques that combine state-of-the-art transformer models with continued pre-training and multi-task learning. however, there is still substantial headroom for improving performance on detecting fine-grained forms of sexist content. we hope that our research and resources can contribute towards the design of future systems that make online spaces safer for all. article. we also thank all participants who submitted results to our task, and our reviewers who gave valuable and constructive comments on the system papers.