Assessing Explainability in Reinforcement Learning

Amber E. Zelvelder*, Marcus Westberg, Kary Främling

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Reinforcement Learning performs well in many different application domains and is starting to receive greater authority and trust from its users. But most people are unfamiliar with how AIs make their decisions and many of them feel anxious about AI decision-making. A result of this is that AI methods suffer from trust issues and this hinders the full-scale adoption of them. In this paper we determine what the main application domains of Reinforcement Learning are, and to what extent research in those domains has explored explainability. This paper reviews examples of the most active application domains for Reinforcement Learning and suggest some guidelines to assess the importance of explainability for these applications. We present some key factors that should be included in evaluating these applications and show how these work with the examples found. By using these assessment criteria to evaluate the explainability needs for Reinforcement Learning, the research field can be guided to increasing transparency and trust through explanations.
Original languageEnglish
Title of host publicationExplainable and Transparent AI and Multi-Agent Systems - 3rd International Workshop, EXTRAAMAS 2021, Revised Selected Papers
EditorsDavide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling
PublisherSpringer
Pages223-240
Number of pages18
ISBN (Print)9783030820169
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event3rd International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021 - Virtual, Online
Duration: 3 May 20217 May 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12688 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021
CityVirtual, Online
Period3/05/217/05/21

Keywords

  • Explainable AI
  • Interpretable Machine Learning
  • Reinforcement Learning
  • XAI

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