Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior
Open accessAbstract
Recent work in HCI suggests that users can be powerful in surfacing harmful algorithmic behaviors that formal auditing approaches fail to detect. However, it is not well understood how users are often able to be so effective, nor how we might support more effective user-driven auditing. To investigate, we conducted a series of think-aloud interviews, diary studies, and workshops, exploring how users find and make sense of harmful behaviors in algorithmic systems, both individually and collectively. Based on our findings, we present a process model capturing the dynamics of and influences on users’ search and sensemaking behaviors. We find that 1) users’ search strategies and interpretations are heavily guided by their personal experiences with and exposures to societal bias; and 2) collective sensemaking amongst multiple users is invaluable in user-driven algorithm audits. We offer directions for the design of future methods and tools that can better support user-driven auditing.
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Recent HCI work on user auditing, such as the 'WeAudit' tool, demonstrates the possibility of designing for a more participatory AI audit process.
“Recent HCI work on user auditing [132], such as the 'WeAudit' tool [126], exemplifies this shift and demonstrates the possibility of designing for a more participatory AI audit process [2, 52, 132].”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [126]
A recent line of HCI research has explored the idea of 'everyday algorithm audits' whereby users detect, understand, and interrogate problematic machine behaviours via their day-to-day interactions with algorithmic systems.
“Indeed, a recent line of HCI research has explored the idea of”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [43]
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Self-citationKollig, F., Pater, J. A., Nova, F. F., & Fiesler, C. L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713322 · Cited by 13 · see citing works
The AI Surge: Transfiguring Audit Profession With AI Technology
Kinana, S. B. B., & Arabiat, O. S. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-756-420251004 · Cited by 12 · see citing works
WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AI
Deng, W. H., Claire, W., Han, H. Z., Hong, J., Holstein, K., & Eslami, M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757702 · Cited by 14 · see citing works
Self-citationLi, W., Kuo, J.-C., Sheng, M., Zhang, P., & Wu, Q. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713241 · Cited by 16 · see citing works
(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court
Jin, A., & Salehi, N. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641902 · Cited by 10 · see citing works
The right to audit and power asymmetries in algorithm auditing
Urman, A., Smirnov, I., & Lasser, J. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00454-5 · Cited by 8 · see citing works
Access Denied: Meaningful Data Access for Quantitative Algorithm Audits
Zaccour, J., Binns, R., & Rocher, L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713963 · Cited by 9 · see citing works
A systematic review of algorithm auditing processes to assess bias and risks in AI systems
Funda, V. (2025) · Journal of Infrastructure Policy and Development · doi:10.24294/jipd11489 · Cited by 14 · see citing works
Imagining the Far East: Exploring Perceived Biases in AI-Generated Images of East Asian Women
Lan, X., An, J., Guo, Y., Chiyou, T., Cai, X., & Zhang, J. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3719678 · Cited by 8 · see citing works
What Is a Subliminal Technique? An Ethical Perspective on AI-Driven Influence
Bermúdez, J. P., Nyrup, R., Deterding, S., Moradbakhti, L., Mougenot, C., You, F., & Calvo, R. A. (2023) · IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS) · doi:10.1109/ethics57328.2023.10155039 · Cited by 8 · see citing works
Xiao, Q., Zheng, Y., Fan, X., Zhang, B., & Lu, Z. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713279 · Cited by 8 · see citing works
Jarke, J., Prietl, B., Egbert, S., Boeva, Y., Heuer, H., & Arnold, M. (2024) · Amsterdam University Press eBooks · doi:10.1515/9789048556908 · Cited by 15 · see citing works
Human-Centered Evaluation and Auditing of Language Models
Liu, Y. L., Deng, W. H., Lam, M. S., Eslami, M., Kim, J., Liao, Q. V., Xu, W., Novikova, J., & Xiao, Z. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3706729 · Cited by 7 · see citing works
Self-citationEslami, M., Fox, S., Shen, H., Fan, B., Lin, Y., Farzan, R., & Schwanke, B. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732173 · Cited by 7 · see citing works
Self-citationLearning AI Auditing: A Case Study of Teenagers Auditing a Generative AI Model
Morales‐Navarro, L., Gan, M. A., Yu, E., Vogelstein, L., Kafai, Y. B., & Metaxa, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757620 · Cited by 7 · see citing works
Impact Assessment Card: Communicating Risks and Benefits of AI Uses
Bogucka, E. P., Constantinides, M., Šćepanović, S., & Quercia, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757482 · Cited by 8 · see citing works
Reflective Design for Informal Participatory Algorithm Auditing: A Case Study with Emotion AI
Howell, N., Hartsoe, W. F., Amin, J., & Namani, V. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3679318.3685411 · Cited by 7 · see citing works
FairFare : A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers
Calacci, D., Rao, V. N., Dalal, S., Di, C., Pua, K.-W., Schwartz, A., Spitzberg, D., & Monroy‐Hernández, A. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3769680 · Cited by 9 · see citing works
Studying Collaborative Interactive Machine Teaching in Image Classification
Mohammadzadeh, B., Françoise, J., Gouiffès, M., & Caramiaux, B. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645204 · Cited by 5 · see citing works
From Routes to Ratings: Challenges and Strategies in Food Delivery Work
Xu, H., Luo, Y., Wang, Y., Wang, D., Pan, Y., & Cai, S. (2025) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-025-09525-1 · Cited by 5 · see citing works
Why am I seeing this: Democratizing End User Auditing for Online Content Recommendations
Chen, C., Li, L., Cao, L., Ye, Y., Li, T., Yao, Y., & Li, T. J.-J. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747798 · Cited by 4 · see citing works
Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors
Lam, M. S., Hohman, F., Moritz, D., Bigham, J. P., Holstein, K., & Kery, M. B. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747680 · Cited by 8 · see citing works
Self-citationIdentifying Algorithmic Decision Subjects' Needs for Meaningful Contestability
Yurrita, M., Verma, H., Balayn, A., Alfrink, K., Gadiraju, U., & Bozzon, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757415 · Cited by 7 · see citing works
Mapping the Smart City: Participatory approaches to XAI
Gupta, S. (2023) · Designing Interactive Systems Conference · doi:10.1145/3563703.3593063 · Cited by 4 · see citing works
Algorithmic Collective Action with Two Collectives
Karan, A., Vincent, N., Karahalios, K. G., & Sundaram, H. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732098 · Cited by 3 · see citing works
Building a Kill Switch for the Terminator: A Case for Slow AI
Trim, M. (2023) · ACM SIGCAS Computers and Society · doi:10.1145/3625671.3625673 · Cited by 3 · see citing works
Correia, A., Schneider, D., Fonseca, B., Mohseni, H., Kujala, T., & Kärkkäinen, T. (2024) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora61326.2024.10550680 · Cited by 4 · see citing works
Liao, J., Wei, Z., Yang, Z., Xu, X., Hui, P., He, C., & Zhou, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713763 · Cited by 3 · see citing works
Making Smart Cities Explainable: What XAI Can Learn from the “Ghost Map”
Gupta, S., & Loukissas, Y. A. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3585847 · Cited by 3 · see citing works
Yeni Medya Bölümü Öğrencilerinin Algoritma Okuryazarlıkları Üzerine Bir Araştırma
Karaman, M. K., & YİĞİT, İ. (2024) · Erciyes İletişim Dergisi · doi:10.17680/erciyesiletisim.1338510 · Cited by 3 · see citing works
Clarify: Improving Model Robustness With Natural Language Corrections
Lee, Y., Lam, M. S., Vasconcelos, H., Bernstein, M. S., & Finn, C. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676362 · Cited by 5 · see citing works
Bialkova, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-56471-0_10 · Cited by 3 · see citing works
Chat with the 'For You' Algorithm: An LLM-Enhanced Chatbot for Controlling Video Recommendation Flow
Niu, S., Vishnuvardhan, D., & Punnam, V. S. R. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736628 · Cited by 3 · see citing works
Vito, P. D. C. S., Fringi, E., Johnston, P., Bezerra, L. C. T., Aristodemou, M., Shahandashti, S. F., O’Hara, E., Whyte, L. F., Luo, L. K., Wong, M., et al. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791757 · Cited by 2 · see citing works
Under manipulations, are some AI models harder to audit?
Godinot, A., Merrer, E. L., Trédan, G., Penzo, C., & Taı̈ani, F. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00038 · Cited by 2 · see citing works
Vogelstein, L., Konda, V., Fields, D. A., Kafai, Y. B., Morales‐Navarro, L., & Metaxa, D. (2025) · Proceedings. · doi:10.22318/icls2025.630877 · Cited by 2 · see citing works
WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AIPreprint
Deng, W. H., Wang, C., Han, H. Z., Hong, J., Holstein, K., & Eslami, M. (2025) · Qeios · doi:10.32388/b1lxnv · Cited by 3 · see citing works
Self-citationGlobal Approaches to Auditing Artificial Intelligence: A Literature Review
(IPIE), I. P. O. T. I. E., Sylvester, P., Chun, W. H. K., Hannák, A., Mendoza, M., Broussard, M., Сергеевич, Е. Р., Nelson, A., Sandvig, C., Sesan, G., et al. (2024) · doi:10.61452/bwym7397 · Cited by 2 · see citing works
How Deep Is Representational Bias in LLMs? The Cases of Caste and ReligionPreprint
Seth, A., Choudhary, M., Sitaram, S., Toyama, K., Vashistha, A., & Bali, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5381652 · Cited by 2 · see citing works
2. Revisiting Transparency Efforts in Algorithmic Regimes
Eslami, M., & Heuer, H. (2024) · Amsterdam University Press eBooks · doi:10.1515/9789048556908-002 · Cited by 2 · see citing works
Self-citationBeyond Bias Detection: Community Auditors and Normative Reasoning in AI Oversight CSCW006
Jackson, C., Ahmad, T., Raj, S. D., & Wu, N. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3788042 · Cited by 1 · see citing works
Chen, Y. A., & Taylor, S. H. (2026) · Communication Research · doi:10.1177/00936502261459750 · Cited by 1 · see citing works
"YOUTUBE DOESN'T CARE ABOUT CREATORS": HOW YOUTUBERS USE THE PLATFORM TO PROMOTE ACCOUNTABILITY
Reynolds, C., & Hallinan, B. (2023) · AoIR Selected Papers of Internet Research · doi:10.5210/spir.v2023i0.13484
Stumpf, S. (2025) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3731406.3742705
Practical Perspectives on Fairness In Technology-Aided Personnel Selection
Müller, I., Dill, K., & Mihaljević, H. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757647
Creating an Effective Methodology for End-User Engagement in AI Auditing
O’Hara, E., Fringi, E., & Simpson, K. (2026) · Advances in intelligent systems and computing · doi:10.1007/978-3-032-07938-1_22
Human-Centered and Participatory AI Auditing
Deng, W. H., Holstein, K., & Eslami, M. (2026) · Springer eBooks · doi:10.1007/978-981-97-8440-0_102-1
Self-citationExploring people’s testing strategies in ML-based image classification
Sanchez, T., Kalamara, F.-M., Stumpf, S., & Caramiaux, B. (2026) · doi:10.1145/3780045.3780051
Zhang, A., & Lee, M. K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791306
Third Workshop on Human-Centered Evaluation and Auditing of Language Models: AI Agents-in-the-Loop
van der Maden, W., Deng, W. H., Liu, Y. L., Jiang, H., Chen, V., Li, H., Kim, J., Liao, Q. V., Xu, W., Eslami, M., et al. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3778709
Self-citationUpadhyay, S., Pruss, D., DeVrio, A., Gajos, K. Z., & Karusala, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791923
Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai'i
Zhao, D., Cha, H., Ryan, M. J., Wang, A., Baker-Ramos, R., Helekahi-Kaiwi, E.-B., Diego, R., Hester, J. D., & Yang, D. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790958 · Cited by 1 · see citing works
Human-Centered and Participatory AI Auditing
Deng, W. H., Holstein, K., & Eslami, M. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_102
Self-citationHumans as Observers: On the Reflexivity of Algorithm Auditing
束, 开., & 张, 昕. (2026) · Global Journal of Media Studies · doi:10.26599/gjms.2026.9330010
Porcaro, L., & Purificato, E. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3802532
Protection or Empowerment? Perspectives on Youth-Inclusive Responsible AI
Solyst, J., Peng, C., Pratapa, P., Wang, C., Ogan, A., Hammer, J., Madaio, M., & Eslami, M. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806124
Self-citation"Are you biased right now, AI?": Investigating Supporting Youths' Systematic Evaluation of GenAI
Solyst, J., Walker, L., Handa, S., Nurdin, F., & Shapiro, R. B. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806125
Rao, P. S. B., Venkatesan, L. N. N., Jayagopi, D. B., & Cherubini, M. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812234
Everyday Auditing of TikTok's Generative AI Manga Filter
Encarnación, R., Morales‐Navarro, L., Kambhamettu, H., & Metaxa, D. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812379
How Probing for Problems and Bias Affects Perceptions of AI Chatbot Trustworthiness
Tolsdorf, J., Luo, A. F., Kodwani, M., Eum, J., Sharif, M., Mazurek, M. L., & Aviv, A. J. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806751
Exploring Backdoor Vulnerabilities and Fairness Challenges in Generative Models: A Survey
Holland, R., Pal, S., & Pan, L. (2026) · ACM Computing Surveys · doi:10.1145/3847107
Gallardo, A., Klucinec, L., Bauer, L., & Cranor, L. F. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3817022
Bhange, M., Alfaghih, M., Creager, E., Sansfaçon, A. P., & Aïvodji, U. (2026) · AI and Ethics · doi:10.1007/s43681-026-01445-0
- Authors
- DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M.
- Affiliations
- Human-Computer Interaction Institute, Carnegie Mellon University, United States
- Carnegie Mellon University (US)
- Venue
- CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2022-04-29
- Pages
- 1-19
- OpenAlex
- W4224992683
- Cited by
- 98
- References
- 43
- Citations by year
- 2026: 21 · 2025: 31 · 2024: 21 · 2023: 23 · 2022: 2
- FWCI
- 19.13
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- Funders
- National Science Foundation (2040942)
- Cisco
- Amazon
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- algorithmic auditing, social bias, search strategies, sensemaking, collective sensemaking, algorithmic transparency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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