Carnegie Mellon University

Block Center for Technology and Society

Block Center for Technology and Society › Responsible AI › Research › Engagement and Co-Design with Stakeholders

Responsible AI Research

Engagement and Co-Design with Stakeholders

Reflecting the voices and values of stakeholders and impacted communities in the design and implementation of AI

2022

Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects

Heidari et al. (2022)

"Give Everybody [..] a Little Bit More Equity": Content Creator Perspectives and Responses to the Algorithmic Demonetization of Content Associated with Disadvantaged Group

Sara Kingsley, Proteeti Sinha, Clara Wang, Motahhare Eslami, and Jason I. Hong (2022)

How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions

Kawakami, Holstein, Wu, et al. (2022)

How Experienced Designers of Enterprise Applications Engage AI as a Design Material

Yildirim et al. (2022)

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

Holstein, Wu, Zhu, et al. (2022)

2021

On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes

Heidari et al. (2021)

Pre-2021

2019

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need

Holstein, et al. (2019)

Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning

Heidari et al. (2019)

WeBuildAI: Participatory Framework for Algorithmic Governance

Lee, Procaccia, et al. (2019)

2018

A Qualitative Exploration of Perceptions of Algorithmic Fairness

Fox, et al. (2018)

2016

Social Justice-Oriented Interaction Design: Outlining Key Design Strategies and Commitments

Fox, et al. (2016)

Contact us | ResponsibleAI@cmu.edu