Worker Discretion Advised: Co-designing Risk Disclosure in Crowdsourced Responsible AI (RAI) Content Work

TL;DR

This work examines how risk disclosure is designed for crowdsourced Responsible AI (RAI) content work, where workers encounter potentially harmful material. Through co-design sessions with task designers, crowdworkers, and platform representatives, the study reveals tensions around specificity, worker agency, and task-designer responsibility across the task lifecycle. It proposes design implications including front-loaded preparation, AI-assisted yet controllable warning tools, negotiated accountability, and enhanced feedback and well-being resources. The findings underscore risk disclosure as a sociotechnical negotiation rather than a mere label, with practical implications for safer, more transparent crowdsourcing pipelines in AI safety work.

Abstract

Responsible AI (RAI) content work, such as annotation, moderation, or red teaming for AI safety, often exposes crowd workers to potentially harmful content. While prior work has underscored the importance of communicating well-being risk to employed content moderators, designing effective disclosure mechanisms for crowd workers while balancing worker protection with the needs of task designers and platforms remains largely unexamined. To address this gap, we conducted co-design sessions with 29 task designers, workers, and platform representatives. We investigated task designer preferences for support in disclosing tasks, worker preferences for receiving risk disclosure warnings, and how platform stakeholders envision their role in shaping risk disclosure practices. We identify design tensions and map the sociotechnical tradeoffs that shape disclosure practices. We contribute design recommendations and feature concepts for risk disclosure mechanisms in the context of RAI content work.

Figures (2)

• Figure 1: Participation survey probe
• Figure 2: AI suggestion based on what other task designers have done