RESEARCH
In the CARE (Collective AI Research and Evaluation) Lab, we study how the knowledge, values, and lived experiences of users, workers, and impacted communities can inform how AI systems are designed, evaluated, and governed. Our research examines forms of AI harm that can be difficult to anticipate through technical evaluation alone, and develops participatory approaches for identifying, understanding, and mitigating these harms in the contexts where AI is actually used.
Current Research
Community-centered AI design, evaluation, and governance
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Large Language Models in Peer-Run Community Behavioral Health Services:
Understanding Peer Specialists and Service Users’ Perspectives on Opportunities,
Risks, and Mitigation Strategies.
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AI Failure Cards: Understanding and Supporting Grassroots Efforts to Mitigate
AI Failures in Homeless Services.
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Understanding Frontline Workers’ and Unhoused Populations’ Perspectives
on AI Used in Homeless Services.
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Everyday Algorithm Auditing: Understanding the Power of Everyday Users
in Surfacing Harmful Algorithmic Behaviors.
Social and relational AI safety
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Beyond the Single Turn: Reframing Refusals as Dynamic Experiences Embedded
in the Context of Mental Health Support Interactions with LLMs.
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The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns
in Large Language Models.
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User-Driven Value Alignment: Understanding Users’ Perceptions and Strategies
for Addressing Biased and Discriminatory Statements in AI Companions.
Human infrastructure of AI safety and evaluation
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Worker Discretion Advised: Co-Designing Risk Disclosure in Crowdsourced
Responsible AI Content Work.
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AURA: Amplifying Understanding, Resilience, and Awareness for Responsible AI Content Work.
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Investigating What Factors Influence Users’ Detection of Harmful Algorithmic
Bias and Discrimination.
Emerging Research Directions
Human-agent collaboration and the future of work
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When Your Boss Is an AI Bot: Exploring Opportunities and Risks
of Manager Clone Agents in the Future Workplace.
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Can GenAI Move from Individual Use to Collaborative Work?
Experiences, Challenges, and Opportunities of Coordinating GenAI
into Collaborative Newswork.
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AI LEGO: Scaffolding Industrial Cross-Functional Collaborations
in Responsible AI During the Early Design Stages.