About Me

I am a PhD student researching identity representations in technical infrastructures. Broadly, I am interested in identity theory, infrastructure studies, AI ethics, and digital identity. I focus primarily on machine learning infrastructures that operationalize human identity characteristics, like gender and race. In particular, I explore the real-world implications of machine learning for those with historically marginalized identities.

I am advised by Jed Brubaker in the Identity Lab.

PDFs of my work can be found on my Research page. Non-publication specific projects can be found under the Projects tab. This includes the HCI Gender Guidelines and my Prelim Reading List documentation.


Select Publications

  1. How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis. Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, Jed R. Brubaker. Proc. ACM Hum.-Comput. Interact. 4, CSCW1, Article 58. May 2020. Best Paper Award. Recognition for Diversity and Inclusion.

  2. How Computers See Gender: An Evaluation of Gender Classification in Commercial Facial Analysis and Image Labeling Services. Morgan Klaus Scheuerman, Jacob M. Paul, Jed R. Brubaker. ACM Hum.-Comput. Interact. 3, CSCW, Article 144. November 2019. Best Paper Award.

  3. Safe Spaces and Safe Places: Unpacking Technology-Mediated Experiences of Safety and Harm with Transgender People. Morgan Klaus Scheuerman, Stacy M Branham, Foad Hamidi. 2018. PACMHCI. Volume 2, CSCW Issue. In Proceedings of the ACM on Human-Computer Interaction,, Vol. 2, CSCW, Article 155. November 2018. Recognition for Diversity and Inclusion.

  4. Gender Recognition or Gender Reductionism? The Social Implications of Automatic Gender Recognition Systems.” Foad Hamidi, Morgan Klaus Scheuerman, Stacy M Branham. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI ’18). April 2018. Best Paper Award.

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