215 citations · 277 across the 14 of their papers we have counts for
16 papers
Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence
Thao Le, Tim Miller, Ruihan Zhang +2
This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human conce…
An Actionability Assessment Tool for Explainable AI
Ronal Singh, Tim Miller, Liz Sonenberg +4
In this paper, we introduce and evaluate a tool for researchers and practitioners to assess the actionability of information provided to users to support algorithmic recourse. Whil…
Explainable agency: human preferences for simple or complex explanations
Michelle Blom, Ronal Singh, Tim Miller +3
Research in cognitive psychology has established that whether people prefer simpler explanations to complex ones is context dependent, but the question of `simple vs. complex' beco…
From Evidence to Decision: Exploring Evaluative AI
Thao Le, Tim Miller, Liz Sonenberg +2
This paper presents a hypothesis-driven approach to improve AI-supported decision-making that is based on the Evaluative AI paradigm - a conceptual framework that proposes providin…
Explaining Model Confidence Using Counterfactuals
Thao Le, Tim Miller, Ronal Singh +1
Displaying confidence scores in human-AI interaction has been shown to help build trust between humans and AI systems. However, most existing research uses only the confidence scor…
Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence
Thao Le, Tim Miller, Ronal Singh +1
In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showin…