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Towards Relatable Explainable AI with the Perceptual Process

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Brian Y LimAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 181, Pages 1 - 24

https://doi.org/10.1145/3491102.3501826

Published: 29 April 2022 Publication History

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Abstract

Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current contrastive explanations are rudimentary comparisons between examples or raw features, which remain difficult to interpret, since they lack semantic meaning. We argue that explanations must be more relatable to other concepts, hypotheticals, and associations. Inspired by the perceptual process from cognitive psychology, we propose the XAI Perceptual Processing Framework and RexNet model for relatable explainable AI with Contrastive Saliency, Counterfactual Synthetic, and Contrastive Cues explanations. We investigated the application of vocal emotion recognition, and implemented a modular multi-task deep neural network to predict and explain emotions from speech. From think-aloud and controlled studies, we found that counterfactual explanations were useful and further enhanced with semantic cues, but not saliency explanations. This work provides insights into providing and evaluating relatable contrastive explainable AI for perception applications.

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Index Terms

Towards Relatable Explainable AI with the Perceptual Process
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 April 2022

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Refereed limited

Funding Sources

Ministry of Education, Singapore

Conference

CHI '22

Sponsor:

SIGCHI

CHI '22: CHI Conference on Human Factors in Computing Systems

April 29 - May 5, 2022

LA, New Orleans, USA

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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