Shion Guha

Human-Centered Data Science, AI and Public Policy, Algorithmic Bias

My current research interests cut across human-computer interaction, data science, and public policy. Recently, I’ve been involved in the development of the field of Human-Centered Data Science, an intersectional research area that combines technical methodologies with interpretive inquiry in order to address biases and structural inequalities in social systems. I am very interested in understanding how algorithmic decision-making processes are designed, implemented and evaluated in public services. In doing so, I often work with marginalized and vulnerable populations and stakeholders in the child welfare system, criminal justice system etc.

An updated list of my publications can be found in my Google Scholar profile.

Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, “Human-Centered Data Science: An Introduction”, MIT Press (forthcoming 2022)

Devansh Saxena, Karla Badillo-Urquiola, Pamela J Wisniewski, Shion Guha. 2021. A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed Through a Case Study of Child Welfare. In Proceedings of the ACM on Human-Computer Interaction (CSCW) (accepted)

Fayika Farhat Nova, Michael Ann DeVito, Pratyasha Saha, Kazi Shohanur Rashid, Shashwata Roy Turzo, Sadia Afrin, and Shion Guha. 2021. Facebook Promotes More Harassment: Social Media Ecosystem, Skill and Marginalized Hijra Identity in Bangladesh. Proc. ACM Hum.-Comput.Interact. 5, CSCW1, Article 157 (April 2021), 35 pages. https://doi.org/10.1145/3449231

Devansh Saxena, Karla Badillo-Urquiola, Pamela J. Wisniewski andShion Guha. 2020. A Human-Centered Review of Algorithms used within the U.S. Child Welfare System. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI 20). Association for Computing Machinery, New York, NY, USA, 115. DOI: https://doi.org/10.1145/3313831.3376229   (Best Paper Honorable Mention, top 5%)

Devansh Saxena, Patrick Skeba, Shion Guha and Eric P. S. Baumer. 2020. Methods for Generating Typologies of Non/use. Proc. ACM Hum.-Comput. Interact. 4, CSCW, Article 27(May 2020), 26 pages. https://doi.org/10.1145/3392832

INF2178H: Experimental Design for Data Science

February 2020: Featured Profile in US Catholic Magazine on AI and Ethics