Identity + Algorithms

Characteristics that make up human identity have become increasingly embedded into technological systems. Human characteristics like age, gender, race, and sexuality are being folded into the categorical structures of automated systems, such as algorithmic computer vision methods. However, these characteristics are often complex, nuanced, and fluid–and linked to social and historical instances of bias and discrimination. The simple and discrete categorization of these characteristics leads to tensions that can clash with human values and identity, and result in risky ramifications for already marginalized populations.

To mitigate the potential risks of these types of technological methods, we are researching ways to appropriately develop algorithms that are sensitive to the nuanced human identities held and expressed by the people classified. Our aim is to inform design approaches that are empowering and safe for all users.


Jed Brubaker, Katie Gach, Aaron Jiang, Anthony Pinter, Morgan Klaus Scheuerman


  1. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets Scheuerman, Morgan Klaus and Weathington, Katy and Mugunthan, Tarun and Denton, Emily and Fiesler, Casey
    Proc. ACM Hum.-Comput. Interact. 7, CSCW1
  2. Taxonomizing and Measuring Representational Harms: A Look at Image Tagging Katzman, Jared and Wang, Angelina and Scheuerman, Morgan Klaus and Blodgett, Su Lin and Laird, Kristen and Wallach, Hanna and Barocas, Solon
  3. Auto-essentialization: Gender in automated facial analysis as extended colonial project Scheuerman, Morgan Klaus and Pape, Madeleine and Hanna, Alex
    Big Data & Society 8, 2: 20539517211053712
  4. How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis Scheuerman, Morgan Klaus and Wade, Kandrea and Lustig, Caitlin and Brubaker, Jed R.
    Proc. ACM Hum.-Comput. Interact. 4, CSCW1: Article 58 Best Paper Honorable Mention
  5. How Computers See Gender: An Evaluation of Gender Classification in Commercial Facial Analysis and Image Labeling Services Scheuerman, Morgan Klaus and Paul, Jacob M and Brubaker, Jed R.
    Proc. ACM Hum.-Comput. Interact. 3, CSCW: Article 144
  6. "Am I Never Going to Be Free of All This Crap?": Upsetting Encounters With Algorithmically Curated Content About Ex-Partners Pinter, Anthony T. and Jiang, Jialun "Aaron" and Gach, Katie Z. and Sidwell, Melanie M. and Dykes, James E. and Brubaker, Jed R.
    Proc. ACM Hum.-Comput. Interact. 3, CSCW: Article 70
  7. Gender is not a Boolean: Towards Designing Algorithms to Understand Complex Human Identities Scheuerman, Morgan Klaus and Brubaker, Jed R.
    In Participation+Algorithms Workshop at CSCW 2018.
  8. Gender Recognition or Gender Reductionism?: The Social Implications of Embedded Gender Recognition Systems Hamidi, Foad and Scheuerman, Morgan Klaus and Branham, Stacy M.
    Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems


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