mlsanityData to 7 Oct 2026

Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice

arXiv 2210.03709 ↗First posted 7 Oct 2022cs.HC cs.AI cs.LGcited by 30

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Abstract (arXiv)

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 complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and the mitigate risks) of user-engaged auditing in industry practice.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 21 of its 102 references that are in the map. First posted on arXiv 7 Oct 2022.

Builds on

21 of 102 references.

References · 21

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
  4. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  5. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  6. 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
  7. 2022Jury Learning: Integrating Dissenting Voices into Machine Learning Modelscited by 115
  8. 2020Participation is not a Design Fix for Machine Learningcited by 110
  9. 2018Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobservedcited by 94
  10. 2013Discrimination in Online Ad Deliverycited by 93
  11. 2022Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governancecited by 67
  12. 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 61
9 more
  1. 2019HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Modelscited by 59
  2. 2018Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failurecited by 49
  3. 2019Roles for Computing in Social Changecited by 49
  4. 2021Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviorscited by 47
  5. 2020Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practicescited by 38
  6. 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 33
  7. 2021Discovering and Validating AI Errors With Crowdsourced Failure Reportscited by 28
  8. 2020What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Designcited by 14
  9. 2021Empowering Local Communities Using Artificial Intelligencecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 9; 2025: 8; 2026: 7.

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  1. 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 81
  2. 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
  3. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  4. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  5. 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
  6. 2023Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMscited by 9
  7. 2024Clarify: Improving Model Robustness With Natural Language Correctionscited by 8
  8. 2025PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teamingcited by 8
  9. 2023Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalismcited by 7
  10. 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
  11. 2025Robust ML Auditing using Prior Knowledgecited by 6
  12. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
18 more
  1. 2024Under manipulations, are some AI models harder to audit?cited by 5
  2. 2024"It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industrycited by 5
  3. 2024Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviorscited by 5
  4. 2023The Value-Sensitive Conversational Agent Co-Design Frameworkcited by 3
  5. 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Courtcited by 3
  6. 2025Attention to Non-Adopterscited by 3
  7. 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
  8. 2025Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasiacited by 2
  9. 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stagescited by 1
  10. 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Toolscited by 1
  11. 2026Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interactioncited by 1
  12. 2026Human-Centred LLM Privacy Audits: Findings and Frictionscited by 1
  13. 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
  14. 2025From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Modelscited by 0
  15. 2026Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemmacited by 0
  16. 2026Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscurescited by 0
  17. 2026PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AIcited by 0
  18. 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0