Samir Passi | DeepAI

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∙ 11/12/2022

Seamful XAI: Operationalizing Seamful Design in Explainable AI

Mistakes in AI systems are inevitable, arising from both technical limit...

0 Upol Ehsan, et al. ∙

∙ 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...

19 Upol Ehsan, et al. ∙

∙ 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...

0 Samir Passi, et al. ∙

∙ 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...

0 Samir Passi, et al. ∙

∙ 02/09/2020

Data Vision: Learning to See Through Algorithmic Abstraction

Learning to see through data is central to contemporary forms of algorit...

0 Samir Passi, et al. ∙

∙ 01/08/2019

Problem Formulation and Fairness

Formulating data science problems is an uncertain and difficult process....

0 Samir Passi, et al. ∙

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