497 citations · 572 across the 7 of their papers we have counts for
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cs.HC2021★ 18 cited
Explainability Pitfalls: Beyond Dark Patterns in Explainable AI
Upol Ehsan, Mark O. Riedl
To make Explainable AI (XAI) systems trustworthy, understanding harmful effects is just as important as producing well-designed explanations. In this paper, we address an important…
cs.LG2021★ 2 cited
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
Ronal Singh, Upol Ehsan, Marc Cheong +2
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the…
cs.HC2021★ 497 cited
Expanding Explainability: Towards Social Transparency in AI systems
Upol Ehsan, Q. Vera Liao, Michael Muller +2
As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in h…