Research

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.

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]