TY - GEN
T1 - How should ai systems talk to users when collecting their personal information? efects of role framing and self-referencing on human-ai interaction
AU - Liao, Mengqi
AU - Sundar, S. Shyam
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/5/6
Y1 - 2021/5/6
N2 - AI systems collect our personal information in order to provide personalized services, raising privacy concerns and making users leery. As a result, systems have begun emphasizing overt over covert collection of information by directly asking users. This poses an important question for ethical interaction design, which is dedicated to improving user experience while promoting informed decision-making: Should the interface tout the benefts of information disclosure and frame itself as a help-provider? Or, should it appear as a help-seeker? We decided to fnd out by creating a mockup of a news recommendation system called Mindz and conducting an online user study (N=293) with the following four variations: AI system as help seeker vs. help provider vs. both vs. neither. Data showed that even though all participants received the same recommendations, power users tended to trust a help-seeking Mindz more whereas non-power users favored one that is both help-seeker and help-provider.
AB - AI systems collect our personal information in order to provide personalized services, raising privacy concerns and making users leery. As a result, systems have begun emphasizing overt over covert collection of information by directly asking users. This poses an important question for ethical interaction design, which is dedicated to improving user experience while promoting informed decision-making: Should the interface tout the benefts of information disclosure and frame itself as a help-provider? Or, should it appear as a help-seeker? We decided to fnd out by creating a mockup of a news recommendation system called Mindz and conducting an online user study (N=293) with the following four variations: AI system as help seeker vs. help provider vs. both vs. neither. Data showed that even though all participants received the same recommendations, power users tended to trust a help-seeking Mindz more whereas non-power users favored one that is both help-seeker and help-provider.
UR - http://www.scopus.com/inward/record.url?scp=85106747351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106747351&partnerID=8YFLogxK
U2 - 10.1145/3411764.3445415
DO - 10.1145/3411764.3445415
M3 - Conference contribution
AN - SCOPUS:85106747351
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
Y2 - 8 May 2021 through 13 May 2021
ER -