About Me

I am a postdoctoral associate in Information Science at CU Boulder researching identity representations in technical infrastructures. I just recently graduted with my PhD in Information Science at CU Boulder, where I was supported by a Microsoft Research PhD Fellowship. I was advised by Jed Brubaker in the Identity Lab.

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.

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. Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development.
    Morgan Klaus Scheuerman, Emily Denton, and Alex Hanna.
    Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 317 (October 2021), 37 pages. Best Paper Award.

  2. 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.

  3. 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.

  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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