Holstein, Kenneth

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?

Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 777

Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms

Holstein, K., McLaren, B. M., & Aleven, V. (2018) · Lecture Notes in Computer Science

Intelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

Cited by 235

Designing for Complementarity: Teacher and Student Needs for Orchestration Support in AI-Enhanced Classrooms

Holstein, K., McLaren, B. M., & Aleven, V. (2019) · Lecture Notes in Computer Science

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 189

A Conceptual Framework for Human–AI Hybrid Adaptivity in Education

Holstein, K., Aleven, V., & Rummel, N. (2020) · Lecture Notes in Computer Science

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 168

Designing for human–AI complementarity in K‐12 education

Holstein, K., & Aleven, V. (2022) · AI Magazine

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 156

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

Kawakami, A., Sivaraman, V., Cheng, H.-F., Stapleton, L., Cheng, Y., Qing, D., Perer, A., Wu, Z. S., Zhu, H., & Holstein, K. (2022) · CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIInformation Systems Theories and Implementation

Cited by 143

Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools

Gmeiner, F., Yang, H., Yao, L., Holstein, K., & Martelaro, N. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Design Education and PracticeUsability and User Interface Design

Cited by 130

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

Deng, W. H., Guo, B., Devrio, A., Shen, H., Eslami, M., & Holstein, K. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 103

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

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 99

Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior

DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022) · CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 98

The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms

An, P., Holstein, K., d'Anjou, B., Eggen, B., & Bakker, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Augmented Reality ApplicationsIntelligent Tutoring Systems and Adaptive Learning

Cited by 69

The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposals

Kawakami, A., Coston, A., Zhu, H., Heidari, H., & Holstein, K. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIE-Government and Public Services

Cited by 54

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

Cabrera, Á. A., Fu, E., Bertucci, D., Holstein, K., Talwalkar, A., Hong, J. I., & Perer, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 51

Pair-Up: Prototyping Human-AI Co-orchestration of Dynamic Transitions between Individual and Collaborative Learning in the Classroom

Yang, K. B., Echeverria, V., Lu, Z., Mao, H., Holstein, K., Rummel, N., & Aleven, V. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 47

Evaluating the impact of human explanation strategies on human-AI visual decision-making

Morrison, K., Shin, D., Holstein, K., & Perer, A. (2023) · Proceedings of the ACM on Human-Computer Interaction

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 37

“A Second Voice”: Investigating Opportunities and Challenges for Interactive Voice Assistants to Support Home Health Aides

Bartle, V., Lyu, J., El Shabazz-Thompson, F., Oh, Y., Chen, A. A., Chang, Y.-J., Holstein, K., & Dell, N. (2022) · CHI Conference on Human Factors in Computing Systems

Social Robot Interaction and HRIInnovative Human-Technology Interaction

Cited by 35

Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?

Holstein, K., & Doroudi, S. (2021) · arXiv

Artificial Intelligence in EducationEthics and Social Impacts of AI

Cited by 29

Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making

Guerdan, L., Coston, A., Wu, Z. S., & Holstein, K. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 28

Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use

Kawakami, A., Coston, A., Heidari, H., Holstein, K., & Zhu, H. (2024) · Proceedings of the ACM on Human-Computer Interaction

Ethics and Social Impacts of AIE-Government and Public Services

Cited by 24

Building, Shifting, & Employing Power: A Taxonomy of Responses From Below to Algorithmic Harm

DeVrio, A., Eslami, M., & Holstein, K. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 21

Replay Enactments: Exploring Possible Futures through Historical Data

Holstein, K., Harpstead, E., Gulotta, R., & Forlizzi, J. (2020) · Proceedings of the 2020 ACM Designing Interactive Systems Conference

Innovative Human-Technology InteractionDesign Education and Practice

Cited by 19

Don’t Be Fooled: The Misinformation Effect of Explanations in Human–AI Collaboration

Spitzer, P., Holstein, J., Morrison, K., Holstein, K., Satzger, G., & Kühl, N. (2026) · International Journal of Human–Computer Interaction

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 12

PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberation

Kuo, T.-S., Chen, Q. Z., Zhang, A. X., Hsieh, J., Zhu, H., & Holstein, K. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionPolitical Theory and Democracy

Cited by 12

Validating LLM-as-a-Judge Systems under Rating Indeterminacy

Guerdan, L., Barocas, S., Holstein, K., Wallach, H., Wu, S., & Chouldechova, A. (2025) · Advances in Neural Information Processing Systems 38

Artificial Intelligence in LawLegal processes and jurisprudence

Cited by 8