🌈 I will be taking on new PhD students in Fall'24. Please apply via the HCII PhD program. I'm also looking for a postdoc in the broad area of Responsible AI, please apply via the CBI Fellowship Program!

I am an Assistant Research Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. 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. A complete list of my publications can be found on my Google Scholar page.

I'm very fortunate to advise and work with the following inspiring students: Jini Kim (co-advised with Jodi Forlizzi), Ningjing Tang (co-advised with Hoda Heidari).

• 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".

• 02/2024: We are organizing the AAAI-24 Workshop on Public Sector LLMs: Algorithmic and Sociotechnical Design. Please come and join us!

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

• 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!

I'm recruiting students who are interested in working on the following three interrelated research questions (but I'm always open to hear your thoughts):

• How can we develop non-expert-oriented toolkits for explainable AI to enable public understanding and interrogation of AI systems? (Sample publication: Design Alternative Representations of Confusion Matrices)


• How can we build community-centered AI to directly engage impacted community members into the design, deployment, evaluation and governance of AI systems that will be deployed in their communities? (Sample publication: The Model Card Authoring Toolkit)


• How can we support buttom-up, collective interventions against harmful machine behaviors in a wide range of digital platforms and algorithmic systems? (Sample publication: Everyday Algorithm Auditing)

SELECTED PUBLICATIONS

• Shen, H., Li, T., Li, T., Park, J., Yang, D. (2023). Shaping the Emerging Norms of Using Large Language Models in Social Computing Research. In Computer Supported Cooperative Work and Social Computing (CSCW’23 Companion). [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.