Generating Interactive Explanations

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Cawsey argues that the user model must be integrated closely with explanation model to provide more natural dialogue.”
A review of explanation methods for Bayesian networks (2002, methodology): “Finally, in the most sophisticated methods, explanation constitutes an ‘intelligent’ dialogue with the system user through natural language by way of interactive methods (Carenini et al ., 1995; Cawsey, 1991; Cawsey, 1993; Cawsey, 1994; Cawsey et al ., 1993; Fiedler, 2001; Moore, 1994).”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “Different ways to structure a conversational explanation, or dialogue, to successfully transfer knowledge from an explainer to an explainee were listed in [114, 115, 116, 117, 81].”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “The most relevant works to this thesis are those that design systems to interactively communicate the ML results [30, 26, 28, 29, 47, 98].”