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.
  User-Driven Value Alignment: Understanding Users’ Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions.
  (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.‡.
  Investigating What Factors Influence Users’ Rating of Harmful Algorithmic Bias and Discrimination.
  (HCOMP’24). [PDF] Best Paper Award 🏆

• Shen H.†, DeVos A.†, Eslami M.‡ and Holstein K.‡.
  Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors.
  (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.
  AI Failure Cards: Understanding and Supporting Grassroots Efforts to Mitigate AI Failures in Homeless Services.
  (FAccT’24). [PDF]

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

• Shen H., Wang L., Deng W., Ciell, Velgersdijk R. and Zhu H.
  The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design.
  (FAccT’22). [PDF]

Responsible AI (RAI) tools, methods and processes

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

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

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