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143220 - Explainable AI and reinforcement learning.pdf (2.79 MB)

Explainable AI and reinforcement learning - a systematic review of current approaches and trends

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journal contribution
posted on 2023-05-20, 21:42 authored by Lindsay WellsLindsay Wells, Bednarz, T
Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as a response to the need for increased transparency and trust in AI. This is particularly important as AI is used in sensitive domains with societal, ethical, and safety implications. Work in XAI has primarily focused on Machine Learning (ML) for classification, decision, or action, with detailed systematic reviews already undertaken. This review looks to explore current approaches and limitations for XAI in the area of Reinforcement Learning (RL). From 520 search results, 25 studies (including 5 snowball sampled) are reviewed, highlighting visualization, query-based explanations, policy summarization, human-in-the-loop collaboration, and verification as trends in this area. Limitations in the studies are presented, particularly a lack of user studies, and the prevalence of toy-examples and difficulties providing understandable explanations. Areas for future study are identified, including immersive visualization, and symbolic representation.

History

Publication title

Frontiers in Artificial Intelligence

Volume

4

Article number

550030

Number

550030

Pagination

48

ISSN

2624-8212

Department/School

School of Information and Communication Technology

Publisher

Frontiers Research Foundation

Place of publication

Switzerland

Rights statement

Copyright © 2021 Wells and Bednarz. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License (https://creativecommons.org/licenses/by/4.0/).

Repository Status

  • Open

Socio-economic Objectives

Artificial intelligence

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