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May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability.

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Title: May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability.
Authors: Zhang, Tong (AUTHOR), Yang, X. Jessie (AUTHOR), Li, Boyang (AUTHOR)
Source: International Journal of Human-Computer Interaction. May2025, Vol. 41 Issue 9, p5623-5647. 25p.
Subjects: Image recognition (Computer vision), Computer vision, Trust, Group reading, Experimental groups
Abstract: Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations in image classification, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. We conduct a human-subject experiment with 120 participants. Half serve as the experimental group and engage in a conversation with a human expert regarding the static explanations, while the other half are in the control group and read the materials regarding static explanations independently. We measure the participants' objective and self-reported comprehension, acceptance, and trust of static explanations. Results show that conversations significantly improve participants' comprehension, acceptance , trust, and collaboration with static explanations, while reading the explanations independently does not have these effects and even decreases users' acceptance of explanations. Our findings highlight the importance of customized model explanations in the format of free-form conversations and provide insights for the future design of conversational explanations. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Tong%22">Zhang, Tong</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+X%2E+Jessie%22">Yang, X. Jessie</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Boyang%22">Li, Boyang</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. May2025, Vol. 41 Issue 9, p5623-5647. 25p.
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  Data: <searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Trust%22">Trust</searchLink><br /><searchLink fieldCode="DE" term="%22Group+reading%22">Group reading</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+groups%22">Experimental groups</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations in image classification, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. We conduct a human-subject experiment with 120 participants. Half serve as the experimental group and engage in a conversation with a human expert regarding the static explanations, while the other half are in the control group and read the materials regarding static explanations independently. We measure the participants' objective and self-reported comprehension, acceptance, and trust of static explanations. Results show that conversations significantly improve participants' comprehension, acceptance , trust, and collaboration with static explanations, while reading the explanations independently does not have these effects and even decreases users' acceptance of explanations. Our findings highlight the importance of customized model explanations in the format of free-form conversations and provide insights for the future design of conversational explanations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2024.2364986
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        Text: English
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        Type: general
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              Text: May2025
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