no code implementations • 12 Nov 2022 • Upol Ehsan, Q. Vera Liao, Samir Passi, Mark O. Riedl, Hal Daume III
We found that the Seamful XAI design process helped users foresee AI harms, identify underlying reasons (seams), locate them in the AI's lifecycle, learn how to leverage seamful information to improve XAI and user agency.
no code implementations • 28 Jul 2021 • Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, Mark O. Riedl
Explainability of AI systems is critical for users to take informed actions.
no code implementations • 15 Feb 2021 • Samir Passi, Phoebe Sengers
In this workshop paper, we use an empirical example from our ongoing fieldwork, to showcase the complexity and situatedness of the process of making sense of algorithmic results; i. e. how to evaluate, validate, and contextualize algorithmic outputs.
no code implementations • 9 Feb 2020 • Samir Passi, Steven J. Jackson
The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work.
no code implementations • 9 Feb 2020 • Samir Passi, Steven J. Jackson
Learning to see through data is central to contemporary forms of algorithmic knowledge production.