Explanatory Dialogs: Towards Actionable, Interactive Explanations

By Gagan Bansal. 2018

7 citations recorded by the source corpus.

Adoption of AI systems in high-stakes domains (e.g., transportation, law, and healthcare) demands that human users trust these systems. A desiderata for establishing trust is that the users understand the system's decision process. However, a high-performing system may use a complex decision process, which may not be interpretable by itself. We argue that existing solutions for generating interpretable explanations have limitations and as a solution, propose developing new explanation systems that enable interactive and actionable dialogs between the user and the system.

Subfields: Artificial Intelligence, Safety Research

How researchers see this paper

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

In Artificial Intelligence

1. Shane T. Mueller
2. Gary Klein
3. Robert R. Hoffman
4. William J. Clancey
5. Abigail Emrey
6. D. Gunning
7. David William Aha
8. Kejkaew Thanasuan

In Safety Research

1. Gary Klein
2. Shane T. Mueller
3. Robert R. Hoffman
4. Ana Marasovic
5. Sarah Wiegreffe

Citations in the corpus

• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “ the pertinent disciplines spanning first generation systems (e.g., Kass and Finn, 1988; Moore and Swartout, 1988) and recent scholarship (Arioua, Buche and Croitoru, 2017; Arioua and Croitoru, 2015; Bansal, 2018; Bansal, Farhadi, and Parikh, 2014; Brezillon and Pomerol, 1997; Core, et al., 2006; Gr”