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After-Action Review for AI (AAR/AI)

Authors:

Jonathan Dodge,

Nicholas Kiddle,

Andrew Anderson,

Caleb Matthews,

Christopher Perdriau,

Margaret Burnett,

Alan FernAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 11, Issue 3-4

Article No.: 29, Pages 1 - 35

https://doi.org/10.1145/3453173

Published: 03 September 2021 Publication History

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Abstract

Explainable AI is growing in importance as AI pervades modern society, but few have studied how explainable AI can directly support people trying to assess an AI agent. Without a rigorous process, people may approach assessment in ad hoc ways—leading to the possibility of wide variations in assessment of the same agent due only to variations in their processes. AAR, or After-Action Review, is a method some military organizations use to assess human agents, and it has been validated in many domains. Drawing upon this strategy, we derived an After-Action Review for AI (AAR/AI), to organize ways people assess reinforcement learning agents in a sequential decision-making environment. We then investigated what AAR/AI brought to human assessors in two qualitative studies. The first investigated AAR/AI to gather formative information, and the second built upon the results, and also varied the type of explanation (model-free vs. model-based) used in the AAR/AI process. Among the results were the following: (1) participants reporting that AAR/AI helped to organize their thoughts and think logically about the agent, (2) AAR/AI encouraged participants to reason about the agent from a wide range of perspectives, and (3) participants were able to leverage AAR/AI with the model-based explanations to falsify the agent’s predictions.

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Cited By

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Hoffman RMueller SKlein GLitman J(2023)Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performanceFrontiers in Computer Science10.3389/fcomp.2023.10962575Online publication date: 6-Feb-2023
https://doi.org/10.3389/fcomp.2023.1096257
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Index Terms

After-Action Review for AI (AAR/AI)
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 11, Issue 3-4

December 2021

483 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3481699

Editor:
Michelle X. Zhou
Juji, Inc., USA

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Publication History

Published: 03 September 2021

Accepted: 01 February 2021

Revised: 01 January 2021

Received: 01 August 2020

Published in TIIS Volume 11, Issue 3-4

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https://doi.org/10.1109/ACCESS.2025.3541424
Hoffman RMueller SKlein GLitman J(2023)Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performanceFrontiers in Computer Science10.3389/fcomp.2023.10962575Online publication date: 6-Feb-2023
https://doi.org/10.3389/fcomp.2023.1096257
Hoffman RMiller TKlein GMueller SClancey W(2023)Increasing the Value of XAI for Users: A Psychological PerspectiveKI - Künstliche Intelligenz10.1007/s13218-023-00806-937:2-4(237-247)Online publication date: 17-Jul-2023
https://doi.org/10.1007/s13218-023-00806-9
Galán JCarrasco RLaTorre A(2022)Military Applications of Machine Learning: A Bibliometric PerspectiveMathematics10.3390/math1009139710:9(1397)Online publication date: 22-Apr-2022
https://doi.org/10.3390/math10091397
Khanna RDodge JAnderson ADikkala RIrvine JShureih ZLam KMatthews CLin ZKahng MFern ABurnett M(2022)Finding AI’s Faults with AAR/AI: An Empirical StudyACM Transactions on Interactive Intelligent Systems10.1145/348706512:1(1-33)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3487065
Tabatabai DRuangrotsakun AIrvine JDodge JShureih ZLam KBurnett MFern AKahng M(2021)“Why did my AI agent lose?”: Visual Analytics for Scaling Up After-Action Review2021 IEEE Visualization Conference (VIS)10.1109/VIS49827.2021.9623268(16-20)Online publication date: Oct-2021
https://doi.org/10.1109/VIS49827.2021.9623268

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