Judgment of the Humanness of an Interlocutor Is in the Eye of the Beholder

By Catherine L. Lortie, Matthieu J. Guitton. 2011

44 citations recorded by the source corpus.

Despite tremendous advances in artificial language synthesis, no machine has so far succeeded in deceiving a human. Most research focused on analyzing the behavior of “good” machine. We here choose an opposite strategy, by analyzing the behavior of “bad” humans, i.e., humans perceived as machine. The Loebner Prize in Artificial Intelligence features humans and artificial agents trying to convince judges on their humanness via computer-mediated communication. Using this setting as a model, we investigated here whether the linguistic behavior of human subjects perceived as non-human would enable us to identify some of the core parameters involved in the judgment of an agents' humanness. We analyzed descriptive and semantic aspects of dialogues in which subjects succeeded or failed to convince judges of their humanness. Using cognitive and emotional dimensions in a global behavioral characterization, we demonstrate important differences in the patterns of behavioral expressiveness of the judges whether they perceived their interlocutor as being human or machine. Furthermore, the indicators of interest displayed by the judges were predictive of the final judgment of humanness. Thus, we show that the judgment of an interlocutor's humanness during a social interaction depends not only on his behavior, but also on the judge himself. Our results thus demonstrate that the judgment of humanness is in the eye of the beholder.

Subfields: Artificial Intelligence, Cultural Studies, Human-Computer Interaction, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cultural Studies

In Human-Computer Interaction

In Cognitive Science & Psychology

Citations in the corpus

Recipes for Safety in Open-domain Chatbots (2020, background): “Hill et al. (2015) observed an almost 30-fold increase in profanity when humans talked to a chatbot (Clever-bot) compared to another human, while Lortie and Guitton (2011) showed that humans display more aggressiveness when believing that their (human) conversation partner is a bot.”
Bot-Adversarial Dialogue for Safe Conversational Agents (2021, background): “Numerous works have shown that humans speak differently with bots than with humans, with increases in profanity and aggressiveness associated with addressing a bot (Hill et al., 2015; Lortie and Guitton, 2011), which motivates the incorporation of human-bot dialogues into our safety framework.”
Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations (2015, methodology): “…evaluating users’ attribution of human qualities or personality traits to the chatbots they converse with, and how that may lead to greater disclosure in medical, research, or therapeutic settings (Hasler, Tuchman, & Friedman, 2013; Holtgraves, Ross, Weywadt, & Han, 2007; Lortie & Guitton, 2011).”
Talking to Bots: Symbiotic Agency and the Case of Tay (2016, background): “Most contemporary artificial agents are still not able to convince people that they are interacting with a real person, in part because they still lack the necessary cognitive abilities and skills (Lortie & Guitton, 2011).”