Worker Discretion Advised: Co-designing Risk Disclosure in Crowdsourced Responsible AI (RAI) Content Work
arXiv:2509.12140v3 Announce Type: replace 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 individual co-design sessions with 15 task designers, 11 crowdworkers, and 3 platform representatives. We investigated task designer preferences for support in disclosing tasks, worker preferences for receiving risk disclosure warnings, and how platform representatives envis
View PDF HTML (experimental)
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 individual co-design sessions with 15 task designers, 11 crowdworkers, and 3 platform representatives. We investigated task designer preferences for support in disclosing tasks, worker preferences for receiving risk disclosure warnings, and how platform representatives 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.
Subjects:
Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
Cite as: arXiv:2509.12140 [cs.HC]
(or arXiv:2509.12140v3 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2509.12140
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3772318.3791558
DOI(s) linking to related resources
Submission history
From: Alice Qian [view email] [v1] Mon, 15 Sep 2025 17:05:34 UTC (7,905 KB) [v2] Tue, 30 Sep 2025 15:57:47 UTC (7,906 KB) [v3] Tue, 31 Mar 2026 19:18:59 UTC (9,161 KB)
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
announceplatformfeature
My forays into cyborgism: theory, pt. 1
In this post, I share the thinking that lies behind the Exobrain system I have built for myself. In another post, I'll describe the actual system. I think the standard way of relating to LLM/AIs is as an external tool (or "digital mind") that you use and/or collaborate with. Instead of you doing the coding, you ask the LLM to do it for you. Instead of doing the research, you ask it to. That's great, and there is utility in those use cases. Now, while I hardly engage in the delusion that humans can have some kind of long-term symbiotic integration with AIs that prevents them from replacing us [1] , in the short term, I think humans can automate, outsource, and augment our thinking with LLM/AIs. We already augment our cognition with technologies such as writing and mundane software. Organizi
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!