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Seamful XAI: Operationalizing Seamful Design in Explainable AI
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.
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Explainability of AI systems is critical for users to take informed actions.
"From What I see, this makes sense": Seeing meaning in algorithmic results
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.
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
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.
Data Vision: Learning to See Through Algorithmic Abstraction
Learning to see through data is central to contemporary forms of algorithmic knowledge production.
