algo-identity

Site for FAT* 2020 CRAFT workshop on algorithmically-encoded identities

View the Project on GitHub alexhanna/algo-identity

Algorithmically-Encoded Identities

Originally presented as a FAT* 2020 CRAFT Session

Our aim with this workshop is to provide a venue within which the FAT* community and others can thoughtfully engage with identity and the categories which are imposed on people as part of making sense of their identities. Most people have nuanced and deeply personal understandings of what identity categories mean to them; however, sociotechnical systems must, through a set of classification decisions, reduce the nuance and complexity of those identities into discrete categories. The impact of misclassifications can range from the uncomfortable (e.g. displaying ads for items that aren’t desirable) to devastating (e.g. being denied medical care; being evaluated as having a high risk of criminal recidivism). Even the act of being classified can force an individual into categories which feel foreign and othering. Through this workshop, we hope to connect participants’ personal understandings of identity to how identity is ‘seen’ and categorized by sociotechnical systems.

Materials

Lecture slides

Terminology cheat sheet

Torquing the Individual

Choose Your Own Administrative Violence Adventure

Classification analytic

Resources

What Census Calls Us

The GenIUSS Group. (2014). [Best practices for Asking Questions to Identify transgender and Other Gender minority respondents on population-based Surveys.(https://williamsinstitute.law.ucla.edu/wp-content/uploads/Survey-Measures-Trans-GenIUSS-Sep-2014.pdf). J.L. Herman (ed.). Los Angeles, CA: the Williams Institute.

Magliozzi et al. (2016). Scaling Up: Representing Gender Diversity in Survey Research. Socius.

Citation

If you use these materials in your courses or workshops, we’d love to know! Please cite the workshop as follows:

@inproceedings{baker2020algorithmically,
  title={Algorithmically encoded identities: reframing human classification},
  author={Baker, Dylan and Hanna, Alex and Denton, Emily},
  booktitle={Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
  pages={681--681},
  year={2020}
}