July 31, 2019 Journal article Open Access

Jonas Gonzalez-Billandon; Jonas Gonzalez-Billandon; Alexander M. Aroyo; Alessia Tonelli; Dario Pasquali; Dario Pasquali; Dario Pasquali; Alessandra Sciutti; Monica Gori; Giulio Sandini; Francesco Rea

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  <identifier identifierType="URL">https://www.openaccessrepository.it/record/59786</identifier>
  <creators>
    <creator>
      <creatorName>Jonas Gonzalez-Billandon</creatorName>
    </creator>
    <creator>
      <creatorName>Jonas Gonzalez-Billandon</creatorName>
    </creator>
    <creator>
      <creatorName>Alexander M. Aroyo</creatorName>
    </creator>
    <creator>
      <creatorName>Alessia Tonelli</creatorName>
    </creator>
    <creator>
      <creatorName>Dario Pasquali</creatorName>
    </creator>
    <creator>
      <creatorName>Dario Pasquali</creatorName>
    </creator>
    <creator>
      <creatorName>Dario Pasquali</creatorName>
    </creator>
    <creator>
      <creatorName>Alessandra Sciutti</creatorName>
    </creator>
    <creator>
      <creatorName>Monica Gori</creatorName>
    </creator>
    <creator>
      <creatorName>Giulio Sandini</creatorName>
    </creator>
    <creator>
      <creatorName>Francesco Rea</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Can a robot catch you lying? A machine learning system to detect lies during interactions.</title>
  </titles>
  <publisher>INFN Open Access Repository</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-07-31</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://www.openaccessrepository.it/record/59786</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3389/frobt.2019.00064</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://www.openaccessrepository.it/communities/itmirror</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Deception is a complex social skill present in human interactions. Many social professions such as teachers, therapists and law enforcement officers leverage on deception detection techniques to support their working activities. Robots with the ability to autonomously detect deception could provide an important aid to human-human and human-robot interactions. The objective of this work is to demonstrate that it is possible to develop a lie detection system that could be implemented on robots. To this goal, we focus on human human and human robot interaction to understand if there is a difference in the behavior of the participants when lying to a robot or to a human. Participants were shown short movies of robberies and then interrogated by a human and by a humanoid robot "detectives". According to the instructions, subjects provided veridical responses to half of the question and false replies to the other half. Behavioral variables such as eye movements, time to respond and eloquence were measured during the task, while personality traits were assessed before experiment initiation. Participant's behavior showed strong similarities during the interaction with the human and the humanoid. Moreover, the behavioral features were used to train and test a lie detection algorithm. The results show that the selected behavioral variables are valid markers of deception both in human-human and in human-robot interactions and could be exploited to effectively enable robots to detect lies. t</description>
  </descriptions>
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