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Automated rationale generation: a technique for explainable AI and its effects on human perceptions

Authors:

Pradyumna Tambwekar,

Brent Harrison,

Mark O. RiedlAuthors Info & Claims

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages 263 - 274

https://doi.org/10.1145/3301275.3302316

Published: 17 March 2019 Publication History

Abstract

Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent's behavior.

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Cited By

Tambwekar PGombolay M(2024)Towards reconciling usability and usefulness of policy explanations for sequential decision-making systemsFrontiers in Robotics and AI10.3389/frobt.2024.137549011Online publication date: 22-Jul-2024
https://doi.org/10.3389/frobt.2024.1375490
Mendez Guzman ESchlegel VBatista-Navarro R(2024)From outputs to insights: a survey of rationalization approaches for explainable text classificationFrontiers in Artificial Intelligence10.3389/frai.2024.13635317Online publication date: 23-Jul-2024
https://doi.org/10.3389/frai.2024.1363531
Herrewijnen ENguyen DBex Fvan Deemter K(2024)Human-annotated rationales and explainable text classification: a surveyFrontiers in Artificial Intelligence10.3389/frai.2024.12609527Online publication date: 24-May-2024
https://doi.org/10.3389/frai.2024.1260952
Show More Cited By

Index Terms

Automated rationale generation: a technique for explainable AI and its effects on human perceptions
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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cover image ACM Conferences

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

March 2019

713 pages

ISBN:9781450362726

DOI:10.1145/3301275

General Chairs:
Wai-Tat Fu,
Shimei Pan,
Program Chairs:
Oliver Brdiczka,
Polo Chau,
Gaelle Calvary

Copyright © 2019 ACM.

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Published: 17 March 2019

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March 17 - 20, 2019

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Cited By

Tambwekar PGombolay M(2024)Towards reconciling usability and usefulness of policy explanations for sequential decision-making systemsFrontiers in Robotics and AI10.3389/frobt.2024.137549011Online publication date: 22-Jul-2024
https://doi.org/10.3389/frobt.2024.1375490
Mendez Guzman ESchlegel VBatista-Navarro R(2024)From outputs to insights: a survey of rationalization approaches for explainable text classificationFrontiers in Artificial Intelligence10.3389/frai.2024.13635317Online publication date: 23-Jul-2024
https://doi.org/10.3389/frai.2024.1363531
Herrewijnen ENguyen DBex Fvan Deemter K(2024)Human-annotated rationales and explainable text classification: a surveyFrontiers in Artificial Intelligence10.3389/frai.2024.12609527Online publication date: 24-May-2024
https://doi.org/10.3389/frai.2024.1260952
Al-Ansari NAl-Thani DAl-Mansoori R(2024)User‐Centered Evaluation of Explainable Artificial Intelligence (XAI): A Systematic Literature ReviewHuman Behavior and Emerging Technologies10.1155/2024/46288552024:1Online publication date: 15-Jul-2024
https://doi.org/10.1155/2024/4628855
Pramanick PRaggioli LRossi ARossi S(2024)Effects of Incoherence in Multimodal Explanations of Robot FailuresCompanion Proceedings of the 26th International Conference on Multimodal Interaction10.1145/3686215.3690155(6-10)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3686215.3690155
Ehsan URiedl M(2024)Explainable AI Reloaded: Challenging the XAI Status Quo in the Era of Large Language ModelsProceedings of the Halfway to the Future Symposium10.1145/3686169.3686185(1-8)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3686169.3686185
Luo SIvison HHan SPoon J(2024)Local Interpretations for Explainable Natural Language Processing: A SurveyACM Computing Surveys10.1145/364945056:9(1-36)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3649450
Alizadeh FTolmie PLee MWintersberger PPins DStevens G(2024)Voice Assistants' Accountability through Explanatory DialoguesProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665557(1-12)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665557
Sanneman LTucker MShah J(2024)An Information Bottleneck Characterization of the Understanding-Workload Tradeoff in Human-Centered Explainable AIProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659032(2175-2198)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659032
Guo GDeng LTandon AEndert AKwon B(2024)MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification ModelsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659011(1861-1874)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659011
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