You are here

Slanted Speculation: Material Encounters with Algorithmic Bias

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 Designing Interactive Systems (DIS). Association for Computing Machinery, New York, NY, USA, 85–99.


Over the past few years, AI bias has become a central concern within design and computing
fields. But as the concept of bias has grown in visibility, its meaning and form have become
harder to grasp. To help designers realize bias, we take inspiration from textile bias (the skew of
woven material) and examine the topic across its myriad forms: visual, textual, and tactile. By
introducing a slanted experience of material and therefore of reality, we explore the translation
of fraught machine learning algorithms into personal and probing artifacts. In this pictorial, we
present nine pieces that materialize complex relationships with machine learning; ground these
relationships in the present and the personal; and point to generative ways of engaging with
biased systems around us.

PDF icon View PDF (7.31 MB)
Research Type: 
Status of Research or Work: