About Me

I am a Research Scientist on Sony AI's AI Ethics team. I am also a Visiting Scholar at University of Colorado Boulder.

Previously, I was a postdoctoral associate in Information Science at CU Boulder researching identity representations in technical infrastructures. I 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. I have conducted consulting work with Twitch's policy team and Microsoft's Responsible AI. I have also interned with Google's Ethical AI team and Meta's Community Integrity team.

Broadly, I am interested in AI ethics and responsible AI, data curation, identity theory, infrastructure studies, 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. Products of Positionality: How Tech Workers Shape Identity Concepts in Computer Vision
   Morgan Klaus Scheuerman and Jed R. Brubaker.
   Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). Best Paper Award.


2. 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.


3. 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.


4. 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.

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