Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice
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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
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- 2018Model Cards for Model Reportingcited by 1,068
- 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
- 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
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- 2022Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governancecited by 67
- 2021Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Supportcited by 61
9 more
- 2019HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Modelscited by 59
- 2018Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failurecited by 49
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- 2020Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practicescited by 38
- 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 33
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Citing papers per year: 2023: 6; 2024: 9; 2025: 8; 2026: 7.
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- 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
- 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
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- 2026Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemmacited by 0
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- 2026Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma28 Feb 2026
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Tools17 Feb 2026
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- 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems26 May 2025
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- 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development25 Feb 2025
- 2024Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors26 Sep 2024
- 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 Industry18 Sep 2024
- 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships17 Jul 2024
- 2024A Taxonomy of Challenges to Curating Fair Datasets10 Jun 2024
- 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court13 Mar 2024
- 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia21 Feb 2024
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- 2024Clarify: Improving Model Robustness With Natural Language Corrections6 Feb 2024
- 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?29 Jan 2024
- 2023Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism12 Nov 2023
- 2023The Value-Sensitive Conversational Agent Co-Design Framework18 Oct 2023
- 2023Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs14 Oct 2023
- 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice10 Jun 2023
- 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI10 Feb 2023
- 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"2 Feb 2023
- 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 81
- 2025PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teamingcited by 8
- 2025From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Modelscited by 0
- 2026PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AIcited by 0
- 2026PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI7 May 2026
- 2025PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming3 Sep 2025
- 2025From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models23 Jul 2025
- 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?29 Jan 2024
- 2023The Value-Sensitive Conversational Agent Co-Design Framework18 Oct 2023
- 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
- 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
- 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
- 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
- 2023Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMscited by 9
- 2024Clarify: Improving Model Robustness With Natural Language Correctionscited by 8
- 2023Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalismcited by 7
- 2024Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationshipscited by 7
- 2025Robust ML Auditing using Prior Knowledgecited by 6
- 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
- 2024Under manipulations, are some AI models harder to audit?cited by 5
- 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
12 most cited of 25 All 25 (JSON)
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- 2026Human-Centred LLM Privacy Audits: Findings and Frictions12 Mar 2026
- 2026Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma28 Feb 2026
- 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Tools17 Feb 2026
- 2026Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interaction13 Feb 2026
- 2025Attention to Non-Adopters10 Oct 2025
- 2025Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia10 Jun 2025
- 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems26 May 2025
- 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages15 May 2025
- 2025Robust ML Auditing using Prior Knowledge7 May 2025
- 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development25 Feb 2025
12 newest of 25 All 25 (JSON)