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Children as Candidates to Verbal Nudging in a Human-robot Experiment

In this research, Nudges, indirect suggestions which can affect the behaviour and the decision making, are considered in the context of conversational machines. A first long term goal of this work is to build an automatic dialog system able to nudge. A second goal is to measure the influence of nudges exerted by conversational agents and robots on humans in order to raise the awareness of their use or misuse and open an ethical reflection on their consequences. The study involved primary school children which are potentially more vulnerable in front of conversational machines. The children verbally interacted in three different setups: with a social robot, a conversational agent and a human. Each setup includes a Dictator Game adapted to children from which we can infer a nudge metric. First results from the Dictator Game highlight that the conversational agent and the robot seem more influential in nudging children than an adult. In this paper, we seek to measure whether the propensity of the children to be nudged can be predicted from personal and overall dialog features (e.g. age, interlocutor, etc.) and expressive behaviour located at speaker turn level (e.g. emotions, etc.). Features are integrated into vectors, with one vector by speaker turn, which are fed to machine learning models. The speakers' characteristics, the type of interlocutor, objective measures at speaker turn level (latency, duration) and also measures built to quantify the reactions to two influencing questions (open-ended and incongruous) correlate best with the reaction to the nudging strategies.