TY - JOUR
T1 - AI in the hands of imperfect users
AU - Kostick-Quenet, Kristin M.
AU - Gerke, Sara
N1 - Funding Information:
K.K.-Q. reports grants from the National Institutes for Health National Center for Advancing Translational Sciences (1R01TR004243-01) and National Institutes for Mental Health (no. 3R01MH125958-02S1). S.G. reports grants from the European Union (Grant Agreement nos. 101057321 and 101057099), the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (Grant Agreement no. 3R01EB027650-03S1), and the Rock Ethics Institute at Penn State University.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML’s human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.
AB - As the use of artificial intelligence and machine learning (AI/ML) continues to expand in healthcare, much attention has been given to mitigating bias in algorithms to ensure they are employed fairly and transparently. Less attention has fallen to addressing potential bias among AI/ML’s human users or factors that influence user reliance. We argue for a systematic approach to identifying the existence and impacts of user biases while using AI/ML tools and call for the development of embedded interface design features, drawing on insights from decision science and behavioral economics, to nudge users towards more critical and reflective decision making using AI/ML.
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U2 - 10.1038/s41746-022-00737-z
DO - 10.1038/s41746-022-00737-z
M3 - Article
C2 - 36577851
AN - SCOPUS:85145232444
SN - 2398-6352
VL - 5
JO - npj Digital Medicine
JF - npj Digital Medicine
IS - 1
M1 - 197
ER -