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      <first_name>Emely</first_name>
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      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
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      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
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      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
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    <title language="eng">Operationalizing Human-Centered Perspectives in Explainable AI</title>
    <parentTitle language="eng">CHI’21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems</parentTitle>
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      <first_name>Upol</first_name>
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      <last_name>Wintersberger</last_name>
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      <first_name>Q. Vera</first_name>
      <last_name>Liao</last_name>
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      <first_name>Martina</first_name>
      <last_name>Mara</last_name>
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      <first_name>Marc</first_name>
      <last_name>Streit</last_name>
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      <first_name>Sandra</first_name>
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      <first_name>Andreas</first_name>
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      <first_name>Mark O.</first_name>
      <last_name>Riedl</last_name>
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      <language>eng</language>
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      <value>Artificial Intelligence</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Explainable AI</value>
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      <language>eng</language>
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    <completedDate>2025-05-28</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">New Frontiers of Human-centered Explainable AI (HCXAI): Participatory Civic AI, Benchmarking LLMs, XAI Hallucinations, and Responsible AI Audits</title>
    <parentTitle language="eng">CHI’25: Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems</parentTitle>
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    <author>
      <first_name>Upol</first_name>
      <last_name>Ehsan</last_name>
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      <first_name>Naomi</first_name>
      <last_name>Yamashita</last_name>
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      <first_name>Elizabeth Anne</first_name>
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      <first_name>Vanessa</first_name>
      <last_name>Evers</last_name>
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      <first_name>Philipp</first_name>
      <last_name>Wintersberger</last_name>
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      <first_name>Koji</first_name>
      <last_name>Yatani</last_name>
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      <first_name>Carina</first_name>
      <last_name>Manger</last_name>
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    <editor>
      <first_name>Xianghua</first_name>
      <last_name>Ding</last_name>
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      <first_name>Nina</first_name>
      <last_name>Hubig</last_name>
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      <first_name>Saiph</first_name>
      <last_name>Savage</last_name>
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    <author>
      <first_name>Justin</first_name>
      <last_name>Weisz</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
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    <id>4832</id>
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    <completedDate>2024-07-03</completedDate>
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    <title language="eng">Human-Centered Explainable AI (HCXAI): Reloading Explainability in the Era of Large Language Models (LLMs)</title>
    <parentTitle language="eng">CHI'24: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems</parentTitle>
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    <author>
      <first_name>Upol</first_name>
      <last_name>Ehsan</last_name>
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    <editor>
      <first_name>Florian 'Floyd'</first_name>
      <last_name>Mueller</last_name>
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      <first_name>Elizabeth Anne</first_name>
      <last_name>Watkins</last_name>
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      <first_name>Penny</first_name>
      <last_name>Kyburz</last_name>
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      <first_name>Philipp</first_name>
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      <first_name>Julie R.</first_name>
      <last_name>Williamson</last_name>
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      <first_name>Carina</first_name>
      <last_name>Manger</last_name>
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    <editor>
      <first_name>Corina</first_name>
      <last_name>Sas</last_name>
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    <author>
      <first_name>Sunnie S. Y.</first_name>
      <last_name>Kim</last_name>
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    <author>
      <first_name>Niels</first_name>
      <last_name>Van Berkel</last_name>
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      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
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    <author>
      <first_name>Mark O</first_name>
      <last_name>Riedl</last_name>
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    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
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  <doc>
    <id>925</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue>8</issue>
    <volume>2020</volume>
    <articleNumber>100214</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-09-19</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Agents, environments, scenarios: A framework for examining models and simulations of human-vehicle interaction</title>
    <abstract language="eng">This paper provides a framework for examining human-vehicle interactions with respect to three dimensions that can involve models or simulations: the agents, the environments, and the scenarios. Agents are considered on a spectrum from human to artificial actors. Environments are considered on a spectrum from simulated to real. Scenarios are considered on a spectrum from constrained to unconstrained. It is argued that these three dimensions capture key differences in research approaches within the field of human-vehicle interaction, and that explicitly situating research and discussions within this framework will allow researchers to better compare and contrast research outcomes and contributions. The framework is used to locate different disciplines in the community with respect to one another, and to identify areas which are as-yet unexplored.</abstract>
    <parentTitle language="eng">Transportation Research Interdisciplinary Perspectives</parentTitle>
    <identifier type="issn">2590-1982</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9259</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.trip.2020.100214</enrichment>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian P.</first_name>
      <last_name>Janssen</last_name>
    </author>
    <author>
      <first_name>Linda Ng</first_name>
      <last_name>Boyle</last_name>
    </author>
    <author>
      <first_name>Wendy</first_name>
      <last_name>Ju</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Ignacio</first_name>
      <last_name>Alvarez</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>theory</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>models</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>human-vehicle interaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>environment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>scenario</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>agent</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fidelity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>control</value>
    </subject>
    <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>
    <collection role="persons" number="62036">Alvarez, Ignacio</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/925/1-s2.0-S2590198220301251-main.pdf</file>
  </doc>
  <doc>
    <id>950</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>21</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>20</volume>
    <articleNumber>1029</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-02-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection</title>
    <abstract language="eng">Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (&gt;92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-9506</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s20041029</enrichment>
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    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Kundinger</last_name>
    </author>
    <author>
      <first_name>Nikoletta</first_name>
      <last_name>Sofra</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>drowsiness detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>driver state</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>simulator study</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>physiological measures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wearable sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
    <collection role="persons" number="59914">Sofra, Nikoletta</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/950/sensors-20-01029-v2-1.pdf</file>
  </doc>
  <doc>
    <id>2589</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>229</pageFirst>
    <pageLast>236</pageLast>
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    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation</title>
    <parentTitle language="eng">Proceedings: 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications</parentTitle>
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