I am an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University, where I direct the CARE (Collective AI Research and Evaluation) Lab. I am also part of the CMU-NIST Cooperative Research Center on AI Measurement Science & Engineering (AIMSEC). I received my PhD from the University of Illinois at Urbana-Champaign.

I'm an interdisciplinary scholar situated at the intersection of human-computer interaction, artificial Intelligence, and public policy. Broadly, I study the social, ethical and policy implications of digital platforms and algorithmic systems. My work has been generously supported by the National Science Foundation, Amazon Research, Cisco Research, Google Research, Microsoft Research, The Public Interest Technology University Network, Block Center for Technology and Society, CyLab, and more.

• 10/2024: Our paper Investigating What Factors Influence Users’ Rating of Harmful Algorithmic Bias and Discrimination has received a Best Paper Award at HCOMP’24!

• 10/2024: We received a planning grant from the National Science Foundation to develop participatory, community-centered AI solutions for strengthening peer-run mental health services.

• 04/2024: We received the 2024 Google Research Scholar Award in Human-Computer Interaction.

• 02/2024: Hong was selected as a Microsoft Research AI & Society Fellow to work on "Supporting the Responsible AI Red-Teaming Human Infrastructure".

• 09/2023: We received a grant from The Public Interest Technology University Network to develop a community-centered toolkit for advancing critical AI literacy.

• 04/2023: Our paper Understanding Frontline Workers’ and Unhoused Populations’ Perspectives on AI Used in Homeless Services has received a Best Paper Award (top 1% of submissions) at CHI'23!

In the CARE (Collective AI Research and Evaluation) Lab, we develop innovative tools, methods, and processes that empower impacted communities, everyday users and the general public to collectively evaluate and mitigate harmful machine behaviors across digital platforms and algorithmic systems. I'm very fortunate to advise and work with the following inspiring PhD students and Postdocs:

PhD students

• Jini Kim (co-advised with Jodi Forlizzi)

• Ningjing Tang (co-advised with Hoda Heidari)

• Qing Xiao

• Alice Qian Zhang (co-advised with Laura Dabbish)

Postdoctoral researcher

• Qiaosi (Chelsea) Wang (CBI Postdoc Fellow co-mentored with Jodi Forlizzi)

SELECTED PUBLICATIONS (BY TOPIC)

A complete list of my publications can be found on my Google Scholar page.

AI auditing, red teaming and alignment

• Fan, X., Xiao, Q., Zhou, X., Pei, J., Sap, M., Lu, Z., Shen, H. (2025). User-Driven Value Alignment: Understanding Users’ Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI’25). [PDF]

• Kingsley, S.†, Zhi, J.†, Deng, W. H., Lee, J., Zhang, S., Eslami, M.‡, Holstein, K.‡, Hong, J.I.‡, Li, T.‡, Shen, H.‡ (2024). Investigating What Factors Influence Users’ Rating of Harmful Algorithmic Bias and Discrimination. In Proceedings of the 12th AAAI Conference on Human Computation and Crowdsourcing (HCOMP’24). [PDF] Best Paper Award 🏆

• Shen H.†, DeVos A.†, Eslami M.‡ and Holstein K.‡ (2021). Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors. In Proc. ACM Hum.-Comput.Interact, 5, 2, Article 433 (CSCW’21). [PDF]

Participatory, community-centered AI design

• Tang, N., Zhi, J., Kuo, T., Kainaroi, C., Northup, J., Holstein, K., Zhu, H., Hedari, H., Shen, H. (2024). AI Failure Cards: Understanding and Supporting Grassroots Efforts to Mitigate AI Failures in Homeless Services. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT’24). [PDF]

• Kuo, T.†, Shen, H.†, Geum, J. S., Jones, N., Hong, J.I., Zhu, H.‡ , Holstein, K.‡ (2023). Understanding Frontline Workers’ and Unhoused Populations’ Perspectives on AI Used in Homeless Services. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI’23). [PDF] Best Paper Award 🏆

• Shen H., Wang L., Deng W., Ciell, Velgersdijk R. and Zhu H. (2022). The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT’22). [PDF]

Responsible AI (RAI) tools, methods and processes

• Kapania, S.†, Wang, R.†, Li, T., Li, T., Shen, H. (2025). “I'm Categorizing LLM as a Productivity Tool”: Examining Ethics of LLM Use in HCI Research Practices. In Proc. ACM Hum.-Comput.Interact (CSCW’25). [PDF]

• Shen, H., Deng W., Chattopadhyay A., Wu Z.S., Wang X and Zhu H. (2021). Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT’21). [PDF] [Teaching Materials]

• Shen, H., Jin H., Cabrera A., Perer A., Zhu H and Hong J. I. (2020). Design Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance. Proc. ACM Hum.-Comput. Interact. 4, Article 153 (CSCW’20). [PDF]

• 05-410/05-610: User-Centered Research and Evaluation (UCRE), School of Computer Science.
• 05-499/05-899: Fairness, Accountability, Transparency, Ethics (FATE) in Sociotechnical Systems, School of Computer Science.
• 90-769/90-442: Critical AI Studies for Public Policy, School of Public Policy and Management.