TY - GEN
T1 - Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence
AU - Yuan, Chien Wen Tina
AU - Bi, Nanyi
AU - Lin, Ya Fang
AU - Tseng, Yuen Hsien
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/4/19
Y1 - 2023/4/19
N2 - Biases in Artificial Intelligence (AI) systems or their results are one important issue that demands AI explainability. Despite the prevalence of AI applications, the general public are not necessarily equipped with the ability to understand how the black-box algorithms work and how to deal with biases. To inform designs for explainable AI (XAI), we conducted in-depth interviews with major stakeholders, both end-users (n = 24) and engineers (n = 15), to investigate how they made sense of AI applications and the associated biases according to situations of high and low stakes. We discussed users' perceptions and attributions about AI biases and their desired levels and types of explainability. We found that personal relevance and boundaries as well as the level of stake are two major dimensions for developing user trust especially during biased situations and informing XAI designs.
AB - Biases in Artificial Intelligence (AI) systems or their results are one important issue that demands AI explainability. Despite the prevalence of AI applications, the general public are not necessarily equipped with the ability to understand how the black-box algorithms work and how to deal with biases. To inform designs for explainable AI (XAI), we conducted in-depth interviews with major stakeholders, both end-users (n = 24) and engineers (n = 15), to investigate how they made sense of AI applications and the associated biases according to situations of high and low stakes. We discussed users' perceptions and attributions about AI biases and their desired levels and types of explainability. We found that personal relevance and boundaries as well as the level of stake are two major dimensions for developing user trust especially during biased situations and informing XAI designs.
KW - AI bias
KW - Artificial Intelligence
KW - Explainability
KW - Explainable AI (XAI)
KW - Human-Centered Computing
KW - Human-Computer Interaction (HCI)
KW - Transparency
UR - http://www.scopus.com/inward/record.url?scp=85160021032&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85160021032&partnerID=8YFLogxK
U2 - 10.1145/3544548.3580945
DO - 10.1145/3544548.3580945
M3 - Conference contribution
AN - SCOPUS:85160021032
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Y2 - 23 April 2023 through 28 April 2023
ER -