Q. Vera Liao

TL;DR: An algorithm-informed XAI question bank is developed in which user needs for explainability are represented as prototypical questions users might ask about the AI, and used as a study probe to identify gaps between current XAI algorithmic work and practices to create explainable AI products.

TL;DR: It is shown that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making, which may also depend on whether the human can bring in enough unique knowledge to complement the AI's errors.

TL;DR: This work introduces AI Explainability 360, an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics, and provides a taxonomy to help entities requiring explanations to navigate the space of explanation methods.

TL;DR: This paper building on the work of other CSCW and HCI researchers in describing the ways that scientists, scholars, engineers, and others work with their data, through analyses of interviews with 21 data science professionals sets five approaches to data along a dimension of interventions.

TL;DR: In this paper, the authors identify gaps between the current XAI algorithmic work and practices to create explainable AI products and develop an algorithm-informed XAI question bank in which user needs for explainability are represented as prototypical questions users might ask about the AI, and use it as a study probe.