research
∙
11/12/2022
Seamful XAI: Operationalizing Seamful Design in Explainable AI
Mistakes in AI systems are inevitable, arising from both technical limit...
share
research
∙
07/28/2021
The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations
Explainability of AI systems is critical for users to take informed acti...
research
∙
02/15/2021
"From What I see, this makes sense": Seeing meaning in algorithmic results
In this workshop paper, we use an empirical example from our ongoing fie...
research
∙
02/09/2020
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
The trustworthiness of data science systems in applied and real-world se...
research
∙
02/09/2020
Data Vision: Learning to See Through Algorithmic Abstraction
Learning to see through data is central to contemporary forms of algorit...
research
∙
01/08/2019
