Abstract
With recent advances in explainable artificial intelligence (XAI), researchers have started to pay attention to concept-level explanations, which explain model predictions with a high level of abstraction. However, such explanations may be difficult to digest for laypeople due to the potential knowledge gap and the concomitant cognitive load. Inspired by recent work, we argue that analogy-based explanations composed of commonsense knowledge may be a potential solution to tackle this issue. In this paper, we propose analogical inference as a bridge to help end-users leverage their commonsense knowledge to better understand the concept-level explanations. Specifically, we design an effective analogy-based explanation generation method and collect 600 analogy-based explanations from 100 crowd workers. Furthermore, we propose a set of structured dimensions for the qualitative assessment of analogy-based explanations and conduct an empirical evaluation of the generated analogies with experts. Our findings reveal significant positive correlations between the qualitative dimensions of analogies and the perceived helpfulness of analogy-based explanations. These insights can inform the design of future methods for the generation of effective analogy-based explanations. We also find that the understanding of commonsense explanations varies with the experience of the recipient user, which points out the need for further work on personalization when leveraging commonsense explanations.
Original language | English |
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Title of host publication | Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing |
Editors | Jane Hsu, Ming Yin |
Pages | 89-101 |
Number of pages | 13 |
DOIs | |
Publication status | Published - 2022 |
Event | HCOMP 2022: 10th AAAI Conference on Human Computation and Crowdsourcing - Duration: 6 Nov 2022 → 10 Nov 2022 |
Publication series
Name | Proceedings of the AAAI Conference on Human Computation and Crowdsourcing |
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Volume | 10 |
ISSN (Print) | 2769-1330 |
ISSN (Electronic) | 2769-1349 |
Conference
Conference | HCOMP 2022: 10th AAAI Conference on Human Computation and Crowdsourcing |
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Period | 6/11/22 → 10/11/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Human-centered Explainable AI
- Analogy
- Concept-level Explanation
- Commonsense Knowledge
Fingerprint
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Analogy-based explanation evaluation dataset for HCOMP 2022 paper "It Is Like Finding a Polar Bear in the Savannah! Concept-level AI Explanations with Analogical Inference from Commonsense Knowledge."
Yang, J. (Creator), Gadiraju, U. K. (Creator), He, G. (Creator), Balayn, A. M. A. (Creator) & Buijsman, S. N. R. (Creator), TU Delft - 4TU.ResearchData, 6 Feb 2025
DOI: 10.4121/DE1DF5C0-5430-40F9-BA9A-BA0D1F415F28
Dataset/Software: Software
Prizes
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Best Paper Award
He, G. (Recipient), Balayn, A.M.A. (Recipient), Buijsman, S.N.R. (Recipient), Yang, J. (Recipient) & Gadiraju, U.K. (Recipient), 2022
Prize: Prize (including medals and awards)