Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Open accessAbstract
Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We highlight aspects of organizational culture that may impact the efficacy of AI fairness checklists, and suggest future design directions.
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Algorithmic systems may introduce structural inequalities through biased or uneven distribution of opportunities (allocation harm).
“At the same time, algorithmic systems may introduce structural inequalities through biased or uneven distribution of opportunities, described as allocation harm (Madaio et al., 2020).”
From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Madaio et al., 2020
Microsoft's AI Fairness checklist was co-designed with practitioners in an effort to be more compatible with practical challenges, but many other standards frameworks did not obviously consult practitioners and fewer involved affected stakeholders.
“Microsoft's AI Fairness checklist, for example, was co-designed with practitioners [39] in an effort to be more compatible with practical challenges, but many of the other standards frameworks we found did not obviously consult practitioners and fewer involved affected stakeholders.”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [39]
Past research in HCI and social computing has developed and studied a host of fairness, explainability, and other toolkits that inform AI evaluations.
“Past research in human-computer interaction (HCI) and social computing has developed and studied a host of fairness, explainability, and other toolkits that inform such evaluations [5, 15, 39, 54, 69, 70, 72, 80, 82, 85, 104, 119, 125, 126, 132].”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [39]
AIEd systems have great potential to address inequities in education, for example by filling gaps in the educational services otherwise available to learners.
“AIEd systems have great potential to address inequities in education, for example by filling gaps in the educational services otherwise available to learners (Madaio et al., 2020; Saxena et al., 2018; Uchidiuno et al., 2018).”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Madaio et al., 2020
The Introduction says AI is used in education and has broad potential for good, but it does not say AIEd systems can address educational inequities or fill gaps in services for learners. Searches for educational inequity, service gaps, and equitable learning opportunities found no passage addressing that claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredAIEd systems can fill gaps by providing educational services where few or none previously existed.
“AIEd systems can fill gaps, providing educational services where few or none previously existed; for example, by offering instruction in the midst of a multi-week teachers' strike, as in the Madaio et al. (2020) case study.”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Madaio et al. (2020)
The Introduction mentions AI systems in education, but does not say they provide services where none previously existed. The Co-Design Workshops section describes fairness-checklist workshops, not instruction during a teachers’ strike. Searches for the strike and educational-service claim found no supporting passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Existing ethical AI guidelines emphasise adherence to ethical principles without complementary translations into actionable practices.
“they emphasise adherence to 'ethical principles' [15] without complementary translations into actionable practices [16,17].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [17]
Issues with current ethical AI checklists include a lack of detailed, technical, and actionable activities to embed ethical principles into AI development, and restricted scope dealing with only a particular ethical concern (e.g. fairness).
“Issues with current ethical AI checklists include a lack of detailed, technical, and actionable activities to embed ethical principles into AI development, and restricted scope dealing with only a particular ethical concern (e.g.fairness) [17].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [17]
Such checklists are an expressed priority when ML practitioners are asked directly about their needs.
“In any case, such checklists are an expressed priority when ML practitioners are asked directly about their needs, and should therefore be co-designed with their close input [2,17,21,192].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [17]
Ethical checklists come with limitations such as the risk of people delegating their thinking to checklists alone.
“Although ethical come with their own limitations (such as the risk of people delegating their thinking to checklists alone) [17], they provide a promising next step for further operationalising ethics in AI for healthcare, one that should be taken in conjunction with advancements in governance and regulation.”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [17]
Current tools are designed for use by those in the production process of AI systems and the key decision-makers around that process, and participation is limited beyond these core stakeholders, except for tools explicitly focused on participation processes.
“As Fig. 2 illustrates, current tools, not surprisingly, are designed for use by those in the production process of AI systems and the key decision-makers around that process.Participation in these tools was found to be limited beyond these core stakeholders, except for tools explicitly focused on participation processes [78].”
From Putting AI ethics to work: are the tools fit for purpose? (2022) · cites this paper as [78]
These discriminatory issues are primarily a reflection of training set deficiencies, namely the underrepresentation of certain parts of the input space and the lack of awareness, consideration and attention of the people building these systems.
“Many of these works conclude that these issues are primarily a refection of training set defciencies, namely the underrepresentation of certain parts of the input space [15,22] and the lack of awareness, consideration and attention of the people building these systems [97,136,146].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [97]
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Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445923 · Cited by 545 · see citing works
Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449081 · Cited by 329 · see citing works
Camilleri, M. A. (2024) · Expert Systems · doi:10.1111/exsy.13406 · Cited by 331 · see citing works
Putting AI ethics to work: are the tools fit for purpose?
Ayling, J., & Chapman, A. (2022) · AI and Ethics · doi:10.1007/s43681-021-00084-x · Cited by 253 · see citing works
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., Yilla-Akbari, N., Gallegos, J., Smart, A., Garcia, E., et al. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604673 · Cited by 279 · see citing works
Design Principles for Generative AI Applications
Weisz, J. D., He, J., Muller, M., Hoefer, G., Miles, R., & Geyer, W. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642466 · Cited by 232 · see citing works
An empirical characterization of fair machine learning for clinical risk prediction
Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103621 · Cited by 164 · see citing works
Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers
Solanki, P., Grundy, J., & Hussain, W. (2023) · AI and Ethics · doi:10.1007/s43681-022-00195-z · Cited by 157 · see citing works
“Since the distribution of ethical issues is not uniform across the AI lifecycle, we suggest that rather than considering a single ethical principle as done in some guidelines [17], developers should focus on the principles most pertinent to the specific stage of the AI lifecycle they are working on.”
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Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support
Madaio, M., Egede, L., Subramonyam, H., Wortman Vaughan, J., & Wallach, H. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512899 · Cited by 156 · see citing works
Self-citationZ-Inspection <sup>®</sup> : A Process to Assess Trustworthy AI
Zicari, R. V., Brodersen, J., Brusseau, J., Dudder, B., Eichhorn, T., Ivanov, T., Kararigas, G., Kringen, P., McCullough, M., Moslein, F., et al. (2021) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2021.3066209 · Cited by 134 · see citing works
Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
Weinberg, L. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13196 · Cited by 127 · see citing works
Dankwa-Mullan, I., Scheufele, E. L., Matheny, M. E., Quintana, Y., Chapman, W. W., Jackson, G., & South, B. R. (2021) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2021.0065 · Cited by 108 · see citing works
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Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Deng, W. H., Nagireddy, M., Lee, M. S. A., Singh, J., Wu, Z. S., Holstein, K., & Zhu, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533113 · Cited by 99 · see citing works
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Ojewale, V., Steed, R., Vecchione, B., Birhane, A., & Raji, I. D. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713301 · Cited by 81 · see citing works
Explainability pitfalls: beyond dark patterns in explainable AI
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How Experienced Designers of Enterprise Applications Engage AI as a Design Material
Yildirim, N., Kass, A., Tung, T., Upton, C., Costello, D., Giusti, R., Lacin, S., Lovic, S., O'Neill, J. M., Meehan, R. O., et al. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517491 · Cited by 71 · see citing works
Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025) · AI and Ethics · doi:10.1007/s43681-023-00331-3 · Cited by 68 · see citing works
Farsight: Fostering Responsible AI Awareness During AI Application Prototyping
Wang, Z. J., Kulkarni, C., Wilcox, L., Terry, M., & Madaio, M. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642335 · Cited by 55 · see citing works
Self-citationPlanning for Natural Language Failures with the AI Playbook
Hong, M. K., Fourney, A., DeBellis, D., & Amershi, S. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445735 · Cited by 54 · see citing works
Kawakami, A., Coston, A., Zhu, H., Heidari, H., & Holstein, K. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642849 · Cited by 54 · see citing works
Varanasi, R. A., & Goyal, N. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580903 · Cited by 52 · see citing works
Ethical and methodological challenges in building morally informed AI systems
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Benjamin, J. J., Lindley, J., Edwards, E., Rubegni, E., Korjakow, T., Grist, D., & Sharkey, R. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660677 · Cited by 32 · see citing works
Lewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30 · see citing works
“, [14, 18, 94, 99, 125]) – both internally (within the organisations responsible for developing and maintaining these systems) and externally (by independent auditors, users and/or regulators).”
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Ayobi, A., Hughes, J., Duckworth, C. J., Dylag, J. J., Gordon James, S., Marshall, P., Guy, M., Kumaran, A., Chapman, A., Boniface, M., et al. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581424 · Cited by 26 · see citing works
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A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication
Luccioni, A. S., Corry, F., Sridharan, H., Ananny, M., Schultz, J., & Crawford, K. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533086 · Cited by 22 · see citing works
Genovesi, S., Mönig, J. M., Schmitz, A., Poretschkin, M., Akila, M., Kahdan, M., Kleiner, R., Krieger, L., & Zimmermann, A. (2024) · AI and Ethics · doi:10.1007/s43681-023-00291-8 · Cited by 19 · see citing works
Question-Driven Design Process for Explainable AI User Experiences
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An Ecosystem Approach to Ethical AI and Data Use: Experimental Reflections
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Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education
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“At the same time, algorithmic systems may introduce structural inequalities through biased or uneven distribution of opportunities, described as allocation harm (Madaio et al., 2020).”
'The plan is just survival': Data Work in Kenya and the Regime of Entrapment
Kapania, S., Yang, T., Abdelkadir, N. A., Scheuerman, M. K., Miceli, M., Taylor, A. S., & Fox, S. E. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791097 · Cited by 2 · see citing works
“…AI studies scholarship has devoted extensive attention to the harms that surface at the point of deployment [13, 37, 117], an emerging line of research has begun to interrogate the infrastructures [16, 81], labor [43, 51, 120], and organizational processes that make AI systems possible [82, 111].”
Building models, building capacity: A review of participatory machine learning for HIV prevention
Sendak, M., Young, M., Kim, J. Y., Hasan, A., Kelsey, C., O’Neal, C., Jagneaux, T., Wilbright, W., Couk, J., Lim, S., et al. (2025) · PLOS Global Public Health · doi:10.1371/journal.pgph.0003862 · Cited by 2 · see citing works
Who Owns AI When It Breaks? The Convergence of AI Governance and Cybersecurity Accountability
Montgomery, J., & Copeland, J. (2026) · The Pinnacle: A Journal by Scholar-Practitioners · doi:10.61643/c79060
- Authors
- Madaio, M. A., Stark, L., Wortman Vaughan, J., & Wallach, H.
- Affiliations
- Carnegie Mellon University (US)
- Microsoft Research Montréal (Canada) (CA)
- Microsoft Research, New York, NY, USA
- Microsoft Research New York City (United States) (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-14
- OpenAlex
- W3014972121
- Cited by
- 436
- References
- 80
- Citations by year
- 2026: 68 · 2025: 72 · 2024: 71 · 2023: 86 · 2022: 61 · 2021: 61 · 2020: 17
- FWCI
- 58.34
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- Microsoft Research
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- algorithmic fairness, AI ethics, co-design, organizational culture, AI ethics principles, organizational challenges, practitioner needs
About these checks
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