I am an Assistant Research Professor in the Human-Computer Interaction Institute at Carnegie Mellon University, where I direct the CARE (Collective AI Research and Evaluation) Lab. 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, communications, and public policy. Broadly, I study the social, ethical and policy implications of digital platforms and algorithmic systems, with a strong emphasis on bias, fairness, social justice and power relations in Artificial Intelligence and Machine Learning. 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 for marginalized populations.

• 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.

• 07/2023: We are organizing a Special Interest Group on Shaping the Emerging Norms of Using LLMs in Social Computing Research at CSCW'23. Please come and join us!

• 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)

• Alice Qian Zhang

Postdoctoral researcher

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

SELECTED PUBLICATIONS

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

• Kapania, S.†, Wang, R.†, Li, T., Li, T., Shen, H. (2024). "I'm Categorizing LLM as a Productivity Tool": Examining Ethics of LLM Use in HCI Research Practices. To Appear in Proc. ACM Hum.-Comput.Interact (CSCW). [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 🏆

• 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), 440-451. [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). (pp. 440-451). [PDF]

• Shen H.†, DeVos A.†, Eslami M.‡ and Holstein K.‡ (2021). Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors. Proc. ACM Hum.-Comput.Interact 5, CSCW2, Article 433 (October 2021). [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). (pp. 850-861). [PDF]

• 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, CSCW2, Article 153 (October 2020). [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.