Gonzalez-Billandon Jonas, Aroyo Alexander M, Tonelli Alessia, Pasquali Dario, Sciutti Alessandra, Gori Monica, Sandini Giulio, Rea Francesco
RBCS, Istituto Italiano di Tecnologia, Genova, Italy.
DIBRIS, University of Genova, Genova, Italy.
Front Robot AI. 2019 Jul 31;6:64. doi: 10.3389/frobt.2019.00064. eCollection 2019.
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 work 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 the possibility to develop a lie detection system that could be implemented on robots. To this goal, we focus on 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.
欺骗是人类互动中存在的一种复杂社交技能。许多社会职业，如教师、治疗师和执法人员，都利用欺骗检测技术来支持他们的工作活动。具备自主检测欺骗能力的机器人可以为人类之间以及人机交互提供重要帮助。这项工作的目标是证明开发一种可在机器人上实现的测谎系统的可能性。为了实现这一目标，我们专注于人与机器人的交互，以了解参与者在对机器人或对人说谎时的行为是否存在差异。向参与者展示抢劫的短片，然后由一名人类和一个类人机器人“侦探”进行询问。根据指示，受试者对一半问题给出真实回答，对另一半问题给出虚假回答。在任务过程中测量了诸如眼球运动、反应时间和口才等行为变量，同时在实验开始前评估了个性特征。参与者在与人类和类人机器人互动时的行为表现出强烈的相似性。此外，这些行为特征被用于训练和测试一种测谎算法。结果表明，所选的行为变量在人与人以及人机交互中都是欺骗的有效指标，并且可以被利用来有效地使机器人能够检测谎言。
