Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice
Wesley Hanwen Deng, Bill Boyuan Guo, Alicia DeVrio, Hong Shen,, Motahhare Eslami, Kenneth Holstein

TL;DR
This paper explores industry practitioners' current practices, challenges, and opportunities in user-engaged algorithm auditing, highlighting organizational obstacles and potential research directions to improve effectiveness and mitigate risks.
Contribution
It provides empirical insights into real-world challenges and organizational factors affecting user-engaged algorithm auditing in industry settings.
Findings
Practitioners face challenges in recruiting and incentivizing user auditors.
Organizational obstacles hinder effective user-engaged auditing.
There are opportunities for HCI research to enhance audit practices and address risks.
Abstract
Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners' current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Ethics and Social Impacts of AI · Open Source Software Innovations
