Abstract
This paper presents a taxonomy of explainability in human–agent systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability, and its relationship to the related terms of interpretability, transparency, explicitness, and faithfulness. These definitions allow us to answer why explainability is needed in the system, whom it is geared to and what explanations can be generated to meet this need. We then consider when the user should be presented with this information. Last, we consider how objective and subjective measures can be used to evaluate the entire system. This last question is the most encompassing as it will need to evaluate all other issues regarding explainability.
Original language | English |
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Pages (from-to) | 673-705 |
Number of pages | 33 |
Journal | Autonomous Agents and Multi-Agent Systems |
Volume | 33 |
Issue number | 6 |
DOIs | |
State | Published - 1 Nov 2019 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Keywords
- Human–agent systems
- Machine learning interpretability
- Machine learning transparency
- XAI