TY - GEN
T1 - Exploring How Users Attribute Responsibilities Across Different Stakeholders in Human-AI Interaction
AU - Chen, Yu Ting
AU - Tsai, Hsin Yi Sandy
AU - Yuan, Chien Wen (Tina)
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/11/13
Y1 - 2024/11/13
N2 - With the increasing integration of artificial intelligence (AI) in various systems and applications, understanding how individuals perceive and assign responsibility in both successful and failed AI interactions is crucial. This study examines the attribution of responsibility among relevant stakeholders - companies, developers, and AI - drawing on the attribution theory. Through an online survey (n = 1,173), we investigated user perceptions of normal and abnormal recommendations on YouTube Kids and their attributions across these stakeholders. Our findings reveal significant differences in perceived ethical responsibility among these stakeholders, with AI consistently bearing higher accountability in both scenarios. This underscores the presence of a complex attribution mechanism in human-AI interactions, calling for a refinement of existing attribution theories to better capture the nuanced dynamics of ethical responsibility in this context.
AB - With the increasing integration of artificial intelligence (AI) in various systems and applications, understanding how individuals perceive and assign responsibility in both successful and failed AI interactions is crucial. This study examines the attribution of responsibility among relevant stakeholders - companies, developers, and AI - drawing on the attribution theory. Through an online survey (n = 1,173), we investigated user perceptions of normal and abnormal recommendations on YouTube Kids and their attributions across these stakeholders. Our findings reveal significant differences in perceived ethical responsibility among these stakeholders, with AI consistently bearing higher accountability in both scenarios. This underscores the presence of a complex attribution mechanism in human-AI interactions, calling for a refinement of existing attribution theories to better capture the nuanced dynamics of ethical responsibility in this context.
KW - artificial intelligence (ai)
KW - attribution
KW - ethical responsibility
KW - human-ai interaction
KW - recommendation systems
KW - stakeholders
UR - https://www.scopus.com/pages/publications/85214577431
UR - https://www.scopus.com/pages/publications/85214577431#tab=citedBy
U2 - 10.1145/3678884.3681852
DO - 10.1145/3678884.3681852
M3 - Conference contribution
AN - SCOPUS:85214577431
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 202
EP - 208
BT - CSCW Companion 2024 - Companion of the 2024 Computer-Supported Cooperative Work and Social Computing
A2 - Bernstein, Michael
A2 - Bruckman, Amy
A2 - Gadiraju, Ujwal
A2 - Halfaker, Aaron
A2 - Ma, Xiaojuan
A2 - Pinatti, Fabiano
A2 - Redi, Miriam
A2 - Ribes, David
A2 - Savage, Saiph
A2 - Zhang, Amy
PB - Association for Computing Machinery
T2 - 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW Companion 2024
Y2 - 9 November 2024 through 13 November 2024
ER -