A Critical Reflection on the Use of Toxicity Detection Algorithms in Proactive Content Moderation Systems

TL;DR

This paper critically examines the deployment of toxicity detection algorithms in proactive content moderation, highlighting socio-technical challenges, potential inequalities, and risks of misuse in real-world applications.

Contribution

It offers a socio-technical analysis of proactive toxicity detection, revealing concerns about inequalities and misuse that are often overlooked in current deployment practices.

Findings

Contextual complexities can exacerbate inequalities in moderation.

Certain user groups may benefit more from proactive interventions.

Risks include misuse, circumvention, and manipulation of algorithms.

Abstract

Toxicity detection algorithms, originally designed with reactive content moderation in mind, are increasingly being deployed into proactive end-user interventions to moderate content. Through a socio-technical lens and focusing on contexts in which they are applied, we explore the use of these algorithms in proactive moderation systems. Placing a toxicity detection algorithm in an imagined virtual mobile keyboard, we critically explore how such algorithms could be used to proactively reduce the sending of toxic content. We present findings from design workshops conducted with four distinct stakeholder groups and find concerns around how contextual complexities may exasperate inequalities around content moderation processes. Whilst only specific user groups are likely to directly benefit from these interventions, we highlight the potential for other groups to misuse them to circumvent detection, validate and gamify hate, and manipulate algorithmic models to exasperate harm.