TY - GEN
T1 - The Impact of Gender and Personality in Human-AI Teaming
T2 - 19th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2023
AU - Milella, Frida
AU - Natali, Chiara
AU - Scantamburlo, Teresa
AU - Campagner, Andrea
AU - Cabitza, Federico
PY - 2023
Y1 - 2023
N2 - This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming.
AB - This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming.
UR - http://www.scopus.com/inward/record.url?scp=85173033697&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-42283-6_19
DO - 10.1007/978-3-031-42283-6_19
M3 - Conference contribution
SN - 9783031422829
VL - II
T3 - Lecture Notes in Computer Science
SP - 329
EP - 349
BT - Human-Computer Interaction – INTERACT 2023
A2 - Abdelnour Nocera, José
A2 - Kristín Lárusdóttir, Marta
A2 - Petrie, Helen
A2 - Piccinno, Antonio
A2 - Winckler, Marco
PB - Springer Nature Switzerland AG
Y2 - 28 August 2023 through 1 September 2023
ER -