RESEARCH

In the CARE (Collective AI Research and Evaluation) Lab, we study how AI systems can be designed, evaluated, and governed in ways that reflect the knowledge, values, and lived experiences of the people and communities they affect. We develop participatory methods for community-centered AI design and governance, study the human infrastructure behind AI safety and evaluation, investigate social and relational harms in human-AI interaction, and explore emerging forms of collaboration between people and AI agents.

Social-relational harms in AI safety

• Beyond the Single Turn: Reframing Refusals as Dynamic Experiences Embedded in the Context of Mental Health Support Interactions with LLMs.
  (FAccT’26). [PDF]

• The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models.
  (CHI’26). Best Paper Honorable Mention Award 🎖 [PDF]

• User-Driven Value Alignment: Understanding Users’ Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions.
  (CHI’25). [PDF]

Community-centered AI design, evaluation, and governance

• Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users’ Perspectives on Opportunities, Risks, and Mitigation Strategies.
  (CHI’26). Best Paper Honorable Mention Award 🎖 [PDF]

• AI Failure Cards: Understanding and Supporting Grassroots Efforts to Mitigate AI Failures in Homeless Services.
  (FAccT’24). [PDF]

• Understanding Frontline Workers’ and Unhoused Populations’ Perspectives on AI Used in Homeless Services.
  (CHI’23). [PDF] Best Paper Award 🏆

The human infrastructure of AI safety and evaluation

• Worker Discretion Advised: Co-Designing Risk Disclosure in Crowdsourced Responsible AI Content Work.
  (CHI’26). [PDF]

• Investigating What Factors Influence Users’ Detection of Harmful Algorithmic Bias and Discrimination.
  (HCOMP’24). [PDF] Best Paper Award 🏆

• Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors.
  (CSCW’21). [PDF]

Human-agent collaboration and the future of work

• When Your Boss Is an AI Bot: Exploring Opportunities and Risks of Manager Clone Agents in the Future Workplace.
  (CHI’26). [PDF]

• Can GenAI Move from Individual Use to Collaborative Work? Experiences, Challenges, and Opportunities of Coordinating GenAI into Collaborative Newswork.
  (CHI’26). [PDF]

• AI LEGO: Scaffolding Industrial Cross-Functional Collaborations in Responsible AI During the Early Design Stages.
  (CSCW’26). [PDF]