Unsupervised Pleasures: Conscientious Datasets for Queer Futures
What does generative AI say about people like me? Can it speak so that I recognize myself? “Unsupervised Pleasures” tries to understand the texts that shape machine learning (ML), by creating exploratory tools and accessible resources to analyze their large-scale datasets and models. It investigates why generative AI systems like GPT-4o have such limited information about anyone who falls outside their norms, and how to realign their values toward digital justice.
“Unsupervised Pleasures” creates software interfaces for interrogating existing training datasets, and it suggests methods to develop alternative datasets using intersectional approaches like community input and contributor consent. It uses critical code studies to examine existing datasets and models; and draws on theories, ethics, and tactics from Black feminists, queer and trans activists, anticolonial and anti-ableist scholars to analyse and reimagine ML tools to better support the communities they serve.
Main Research Topics
- Intersectional AI
- Critical Code Studies
- Data Justice
- Machine Learning
- Surveillance Studies
- Artistic Research
Curriculum Vitae
- PhD Media Arts + Practice, University of Southern California, 2017–2024
- Google Season of Docs Fellow, “Critical AI Tutorials,” Processing Foundation, 2024
- Research Associate, “Calculating Empires,” Kate Crawford and Vladan Joler, 2023
- Junge Akademie Fellow, “AI Anarchies”, Akademie der Künste, Berlin, 2022-2023
- Fellow, AI & Society Lab, Humboldt Institute for Internet and Society, 2021
Publications and Presentations
- Ciston, S. (2023). A Critical Field Guide for Working with Machine Learning Datasets (M. Ananny & K. Crawford, Eds.). doi.org/10.48550/ARXIV.2501.15491
https://knowingmachines.org/critical-field-guide - Ciston, S., Mann, Z., Marino, M. C., & Douglass, J. (2025) Can Open-Source Fix Predictive Policing? An Anti-Racist Critical Code Studies Approach to Contemporary AI Policing Software. Digital Humanities Quarterly. https://www.digitalhumanities.org/dhq/vol/19/1/000757/000757.html
- Ciston, S. (2020). Intersectional AI Toolkit. https://intersectionalai.com/
- Ciston, S. (2019). Intersectional AI Is Essential: Polyvocal, Multimodal, Experimental Methods to Save Artificial Intelligence. Journal of Science and Technology of the Arts, 11(2), 3–8. https://doi.org/10.7559/citarj.v11i2.665
