Towards Ethics by Design in Online Abusive Content Detection

TL;DR

This paper proposes a unified, ethics-driven framework for online abusive content detection that categorizes content and assesses abuse severity, aiming to improve accuracy and trustworthiness in NLP models.

Contribution

It introduces a novel two-step framework guided by Ethics by Design, consolidating diverse tasks and addressing ethical issues in abusive content detection.

Findings

Framework categorizes content by personal and identity-related topics.

Severity of abuse is determined through comparative annotation.

Aims to enhance model accuracy and ethical trustworthiness.

Abstract

To support safety and inclusion in online communications, significant efforts in NLP research have been put towards addressing the problem of abusive content detection, commonly defined as a supervised classification task. The research effort has spread out across several closely related sub-areas, such as detection of hate speech, toxicity, cyberbullying, etc. There is a pressing need to consolidate the field under a common framework for task formulation, dataset design and performance evaluation. Further, despite current technologies achieving high classification accuracies, several ethical issues have been revealed. We bring ethical issues to forefront and propose a unified framework as a two-step process. First, online content is categorized around personal and identity-related subject matters. Second, severity of abuse is identified through comparative annotation within each category. The novel framework is guided by the Ethics by Design principle and is a step towards building more accurate and trusted models.