Michael Madaio
Michael Madaio is a Senior Research Scientist at Google Research focusing on human-centered and responsible AI. His research draws on methods and theories from human-computer interaction and the learning sciences to understand the societal impacts of AI and enable the responsible development and use of AI systems. His research has received several best paper awards, including at the ACM Conference on Fairness, Accountability, and Transparency (FAccT), the ACM Conference on Human Factors in Computing Systems (CHI), the International Conference of the Learning Sciences (ICLS), the International Conference on AI in Education (AIED), and others.
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Who Controls the Curriculum for AI? The Limits of Participatory Design for Educational AI
Learning Under Algorithmic Conditions, University of Minnesota Press (2026)
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Participatory design is a long-standing effort to shift control over technology design from technologists to users and communities impacted by technologies. For educational AI, this means involving students, families, teachers, and other stakeholders in shaping the design of AI systems. While promising, in this article, I situate the recent calls for participatory design of educational AI systems within a different historical tradition—that of contests over local control of educational curricula. I argue that approaches that attempt to steer the design and development of educational AI through participatory methods may inadvertently reproduce the history of political contestation of educational curricula, in ways that may privilege the most powerful communities, rather than those inequitably impacted. What might it look like to treat participatory AI design as a site for political contestation? How might these approaches avoid reproducing the same majoritarian tendencies that led to educational inequities in the first place?
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Responsible AI (RAI) practices are increasingly important for practitioners in anticipating and addressing potential harms of AI, and emerging research suggests that AI practitioners often learn about RAI on-the-job. More generally, learning at work is social; thus, this work explores the interpersonal aspects of learning about RAI on-the-job. Through workshops with 21 industry-based RAI educators, we offer the first empirical investigation into interpersonal processes and dimensions of learning about RAI at work. This study finds key phases of RAI are sites for ongoing interpersonal learning, such as critical reflection about potential RAI impacts and collective sense-making about operationalizing RAI principles. We uncover a significant gap between these interpersonal learning processes and current approaches to learning about RAI. Finally, we identify barriers and supports for interpersonal learning about RAI. We close by discussing opportunities to better enable interpersonal learning about RAI on-the-job and the broader implications of interpersonal learning for RAI.
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Machine learning (ML) fairness evaluation in real-world, industry settings presents unique challenges due to business-driven constraints that influence decision-making processes. While prior research has proposed fairness frameworks and evaluation methodologies, these approaches often focus on idealized conditions and may lack consideration for the practical realities faced by industry practitioners. To understand these practical realities, we conducted a semi-structured interview study with 21 experts from academia and industry specializing in ML fairness. Through this study, we explore three constraints of ML fairness evaluation in industry— balancing competing interests, lacking power/access, and getting buy-in—and how these constraints lead to satisficing, seeking satisfactory rather than ideal outcomes. We define the path from these constraints to satisficing as pragmatic fairness. Using recommender systems as a case study, we explore how practitioners navigate these constraints and highlight actionable strategies to improve fairness evaluations within these business-minded boundaries. This paper provides practical insights to guide fairness evaluations in industry while also showcasing how the FAccT community can better align research goals with the operational realities of practitioners.
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"Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizations
Sanika Moharana
Erin Buehler
Vinita Tibdewal
Proceedings of AIES 2025 (2025) (to appear)
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The rapid emergence of generative AI models and AI powered systems has surfaced a variety of concerns around responsibility, safety, and inclusion. Some of these concerns address specific vulnerable communities, including people with disabilities. At the same time, these systems may introduce harms upon disabled users that do not fit neatly into existing accessibility classifications, and may not be addressed by current accessibility practices. In this paper, we investigate how stakeholders across a variety of job types are encountering and addressing potentially negative impacts of AI on users with disabilities. Through interviews with 25 practitioners, we identify emerging challenges related to AI’s impact on disabled users, systemic obstacles that contribute to problems, and effective strategies for impacting change. Based on these findings, we offer suggestions for improving existing processes for creating AI-powered systems and supporting practitioners in developing skills to address these emerging challenges.
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Machine learning (ML) fairness evaluation in real-world, industry settings presents unique challenges due to business-driven constraints that influence decision-making processes. While prior research has proposed fairness frameworks and evaluation methodologies, these approaches often focus on idealized conditions and may lack consideration for the practical realities faced by industry practitioners. To understand these practical realities, we conducted a semi-structured interview study with 21 experts from academia and industry specializing in ML fairness. Through this study, we explore three constraints of ML fairness evaluation in industry— balancing competing interests, lacking power/access, and getting buy-in—and how these constraints lead to satisficing, seeking satisfactory rather than ideal outcomes. We define the path from these constraints to satisficing as pragmatic fairness. Using recommender systems as a case study, we explore how practitioners navigate these constraints and highlight actionable strategies to improve fairness evaluations within these business-minded boundaries. This paper provides practical insights to guide fairness evaluations in industry while also showcasing how the FAccT community can better align research goals with the operational realities of practitioners.
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Preview abstract
Machine learning (ML) fairness evaluation in real-world, industry settings presents unique challenges due to business-driven constraints that influence decision-making processes. While prior research has proposed fairness frameworks and evaluation methodologies, these approaches often focus on idealized conditions and may lack consideration for the practical realities faced by industry practitioners. To understand these practical realities, we conducted a semi-structured interview study with 21 experts from academia and industry specializing in ML fairness. Through this study, we explore three constraints of ML fairness evaluation in industry— balancing competing interests, lacking power/access, and getting buy-in—and how these constraints lead to satisficing, seeking satisfactory rather than ideal outcomes. We define the path from these constraints to satisficing as pragmatic fairness. Using recommender systems as a case study, we explore how practitioners navigate these constraints and highlight actionable strategies to improve fairness evaluations within these business-minded boundaries. This paper provides practical insights to guide fairness evaluations in industry while also showcasing how the FAccT community can better align research goals with the operational realities of practitioners.
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Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges
Qiaosi Wang
Shivani Kapania
Lauren Wilcox
ACM Conference on Human Factors in Computing Systems (ACM CHI) 2023, ACM (2023)
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The shift towards Responsible AI (RAI) in the tech industry necessitates new practices and adaptations to roles. To understand practices at the intersection of user experience (UX) and RAI, we conducted an interview study with industrial UX practitioners and RAI subject matter experts, both of whom are actively involved in addressing RAI concerns, both early in and throughout the development of new AI-based prototypes, demos, and products. Many of the specific practices and their associated challenges have yet to be surfaced, and distilling them offers a critical view into how practitioners' roles are adapting to meet present-day RAI challenges. We present and discuss three emerging practices in which RAI is being enacted and reified in UX work. We conclude by arguing that the emerging practices, goals, and types of expertise that surfaced in our study point to an evolution in praxis that suggests important areas for further research in HCI.
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