Logan Stapleton
Logan Stapleton is an Assistant Professor of Computer Science. They teach on data science, machine learning, and societal implications thereof. They research AI-based technologies used in the child welfare system, and suicide prevention from a critical lens. They are a PhD candidate from the University of Minnesota.
Logan Stapleton is an Assistant Professor of Computer Science. Their teaching focuses on data science, machine learning, AI, and the political, social, and ethical implications of new technologies. They research new technologies used in the child welfare system, local governments, suicide prevention, and mental health support. They’re concerned with how these technologies perpetuate systemic violence, like carcerality or racism, and how we should design technologies differently. They are a PhD candidate at the University of Minnesota and received their BA from Macalester College.
Research and Academic Interests
My research focuses on new technologies used in care domains like the child welfare system, suicide prevention, and mental health support to rectify systemic violence, like carcerality and racism. My work is at the intersection of fields like Human-Computer Interaction (HCI), critical algorithm studies, computer ethics, and machine learning.
Departments and Programs
Selected Publications
"If This Person is Suicidal, What Do I Do?": Designing Computational Approaches to Help Online Volunteers Respond to Suicidality. 2024. Logan Stapleton, Sunniva Liu, Cindy Liu, Irene Hong, Stevie Chancellor, Robert E Kraut, and Haiyi Zhu. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, New York, NY, USA, Article 533, 1–21.
How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions. 2022. Hao-Fei Cheng*, Logan Stapleton*, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, and Haiyi Zhu (*equal contribution). In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 162, 1–22.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. 2022. Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, and Haiyi Zhu. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 1162–1177.
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