This paper presents a survey of issues relating to 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 needs to evaluate all other issues regarding explainability.
|Title of host publication||Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020|
|Editors||Bo An, Amal El Fallah Seghrouchni, Gita Sukthankar|
|Publisher||International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)|
|Number of pages||4|
|State||Published - 2020|
|Event||19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand|
Duration: 19 May 2020 → …
|Name||Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS|
|Conference||19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020|
|Period||19/05/20 → …|
Bibliographical notePublisher Copyright:
© 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.
- Human-agent systems
- Machine learning interpretability
- Machine learning transparency