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    <id>6474</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>34</issue>
    <volume>2025</volume>
    <articleNumber>101742</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Balancing transparency and control: The impact of AI explanation detail on user perception in automated vehicles</title>
    <abstract language="eng">As automated vehicle technology advances, explainable AI has emerged as a critical tool to enable users to understand and predict the behavior of AI systems, particularly in safety-critical applications such as automated driving. However, increased transparency in AI explanations may inadvertently contribute to an “illusion of control”, a cognitive bias in which drivers overestimate their influence or understanding of the AI’s actions. We aim to better understand how the level of detail in AI explanations affects users of automated vehicles. In a virtual reality study, N = 44 participants experienced different explanation levels (low, medium, high) in an automated ride (SAE L4) compared to a baseline condition with no explanations. The results show a significant improvement in participants’ user experience, acceptance, and explanation satisfaction, with more detailed explanations. Our findings also indicate that as AI explanations become more detailed, users’ perceived level of control increases significantly, although this perception does not correlate with actual control capabilities. At the same time, it decreased their desire to take control, indicating users’ susceptibility to the ’illusion of control’ bias in the context of automated driving. Overall, this suggests that the design of explanation interfaces should strive for a balanced level of detail that promotes AI transparency without causing cognitive overload. At the same time, explainable AI can be utilized to decrease users’ desire to intervene in the AI’s actions.</abstract>
    <parentTitle language="eng">Transportation Research Interdisciplinary Perspectives</parentTitle>
    <identifier type="issn">2590-1982</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64740</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Jakob</first_name>
      <last_name>Peintner</last_name>
    </author>
    <author>
      <first_name>Carina</first_name>
      <last_name>Manger</last_name>
    </author>
    <author>
      <first_name>Melanie</first_name>
      <last_name>Berger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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