# WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AI

arXiv: https://arxiv.org/abs/2501.01397 · HTML: https://mlsanity.com/paper/2501.01397/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2025.

## Built on by (most cited first, 14 of 14)

- [PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming](https://mlsanity.com/paper/2509.03728/) (2025)
- [What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparedness](https://mlsanity.com/paper/2509.21868/) (2025)
- [PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A](https://mlsanity.com/paper/2602.21045/) (2026)
- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [Human-Centred LLM Privacy Audits: Findings and Frictions](https://mlsanity.com/paper/2603.12094/) (2026)
- [A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology](https://mlsanity.com/paper/2507.16207/) (2025)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)
- [Seeing Twice: How Side-by-Side T2I Comparison Changes Auditing Strategies](https://mlsanity.com/paper/2511.21547/) (2025)
- [MM-SCALE: Grounded Multimodal Moral Reasoning via Scalar Judgment and Listwise Alignment](https://mlsanity.com/paper/2602.03665/) (2026)
- [PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI](https://mlsanity.com/paper/2605.05682/) (2026)
- [Beyond Third-Person Audits: Situated Interaction Auditing for User-Centered LLM Bias Research](https://mlsanity.com/paper/2606.12247/) (2026)
- [AISPA: User-Centric System Prompt Auditing for Large Language Model Applications](https://mlsanity.com/paper/2607.28617/) (2026)
- [Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level](https://mlsanity.com/paper/2608.14692/) (2026)
- [Small-world Networks of Agents Brainstorm AI Risks to Support Ideation](https://mlsanity.com/paper/2609.24859/) (2026)

## Abstract (arXiv)

There has been growing interest from both practitioners and researchers in engaging end users in AI auditing, to draw upon users' unique knowledge and lived experiences. However, we know little about how to effectively scaffold end users in auditing in ways that can generate actionable insights for AI practitioners. Through formative studies with both users and AI practitioners, we first identified a set of design goals to support user-engaged AI auditing. We then developed WeAudit, a workflow and system that supports end users in auditing AI both individually and collectively. We evaluated WeAudit through a three-week user study with user auditors and interviews with industry Generative AI practitioners. Our findings offer insights into how WeAudit supports users in noticing and reflecting upon potential AI harms and in articulating their findings in ways that industry practitioners can act upon. Based on our observations and feedback from both users and practitioners, we identify several opportunities to better support user engagement in AI auditing processes. We discuss implications for future research to support effective and responsible user engagement in AI auditing and red-teaming.