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Additional Options

Algorithmic Bias

  • Adams, H.B., Applegarth, R., & Simpson, A.H. (2020, Sept.). Acting with algorithms: Feminist propositions for rhetorical agency. Computers and Composition, 57. https://doi.org/10.1016/j.compcom.2020.102581
  • Algorithms for her? 2: Feminist approaches to digital infrastructures, cultures and economies [Special issue]. Journal of Gender Studies, 34(8). https://www.tandfonline.com/toc/cjgs20/34/8 
  • Browne, J., Cave, S., Drage, E., & McInerney, K. (Eds.). (2024). Feminist AI: Critical perspectives on algorithms, data, and intelligent machines. Oxford University Press.
  • Castaneda, J., Jover, A., Calvet, L., Yanes, S., et al. (2022). Dealing with gender bias issues in data-algorithmic processes: A social-statistical perspective. Algorithms, 15(9), 303. https://doi.org/10.3390/a15090303 
  • Doyle-Burke, D., & Smith, J.J. (Hosts). (2020, Apr. 9). Have classification algorithms gone too far? Exploring gender in AI with Morgan Klaus Scheuerman (Episode 4) [Audio podcast episode]. In The Radical AI Podcast. Radical AI LLC. https://www.radicalai.org/e4-morgan-scheuerman 
  • Doyle-Burke, D., & Smith, J.J. (Hosts). (2023, Mar. 22). More than a Glitch, technochauvanism, and algorithmic accountability with Meredith Broussard (Episode 88) [Audio podcast episode]. In The Radical AI Podcast. Radical AI LLC. https://www.radicalai.org/more-than-a-glitch 
  • Gajjala, R., Faniyi, O.M., et al. (2024). Get the hammer out! Breaking computational tools for feminist, intersectional “small data” research. Journal of Digital Social Research, 6(2). https://doi.org/10.33621/jdsr.v6i2.193 
  • Hall, P., & Ellis, D. (2023). A systematic review of socio-technical gender bias in AI algorithms. Online Information Review, 47(7), 1264–1279. https://doi.org/10.1108/OIR-08-2021-0452 
  • Huang, L.T.-L., Chen, H.-Y., Lin, Y.-T., Huang, T.-R., & Hung, T.-W. (2022). Ameliorating algorithmic bias, or why explainable AI needs feminist philosophy. Feminist Philosophy Quarterly, 8(3/4). https://doi.org/10.5206/fpq/2022.3/4.14347 
  • Johnson, G.M. (2023). Are algorithms value-free?: Feminist theoretical virtues in machine learning. Journal of Moral Philosophy, 21(1-2), 27-61. https://doi.org/10.1163/17455243-20234372 
  • Kong, Y. (2022). Are “intersectionally fair” AI algorithms really fair to women of color? A philosophical analysis. FAccT ’22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 485-494. https://doi.org/10.1145/3531146.3533114  
  • Marčetić, H., & Nolin, J. (2022). Feminist data studies and the emergence of a new data feminist knowledge domain. First Monday, 27(7). https://doi.org/10.5210/fm.v27i7.12295
  • Myers West, S. (2020, Nov. 7). Redistribution and rekognition: A feminist critique of algorithmic fairness. Catalyst, 6(2). https://doi.org/10.28968/cftt.v6i2.33043 
  • O’Connor, S., & Liu, H. (2024). Gender bias perpetuation and mitigation in AI technologies: Challenges and opportunities. AI & Society, 39, 2045–2057. https://doi.org/10.1007/s00146-023-01675-4 
  • Swist, T., Humphry, J., & Gulson, K.N. (2023). Pedagogic encounters with algorithmic system controversies: A toolkit for democratising technology. Learning, Media and Technology, 48(2), 226–239. https://doi.org/10.1080/17439884.2023.2185255