Slanted Speculations

2022

Slanted Speculations is the result of a course on algorithmic bias offered at the University of Washington since 2021. The course explores algorithmic bias through material and speculative design, rethinking bias not as a flaw to be eliminated but as a creative and conceptual tool. Drawing from the textile metaphor of “bias” as a skew or slant in fabric, this work examines how bias might be repurposed for more generative engagements with machine learning systems.

This pictorial presents a series of nine speculative projects which materialize AI bias in visual, textual, and tactile forms—from GAN-generated images and algorithmic poetry to physical artifacts that reveal hidden aspects of data systems. Instead of attempting to “fix” biased algorithms, the project develops a “slanted speculation” approach, which skews conventional narratives of AI and challenges dominant paradigms of fairness, objectivity, and computation.

Gabrielle Benabdallah, Ashten Alexander, Sourojit Ghosh, Chariell Glogovac-Smith, Lacey Jacoby, Caitlin Lustig, Anh Nguyen, Anna Parkhurst, Kathryn Reyes, Neilly H. Tan, Edward Wolcher, Afroditi Psarra, and Daniela Rosner. 2022. Slanted Speculations: Material Encounters with Algorithmic Bias. In Proceedings of the 2022 ACM Designing Interactive Systems Conference (DIS '22). Association for Computing Machinery, New York, NY, USA, 85–99. https://doi.org/10.1145/3532106.3533449

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