Referierte Aufsätze Web of Science
Michael Allan Ribers, Hannes Ullrich
In: Quantitative Marketing and Economics (2024), im Ersch. [online first: 2024-07-05]
Artificial Intelligence has the potential to improve human decisions in complex environments, but its effectiveness can remain limited if humans hold context-specific private information. Using the empirical example of antibiotic prescribing for urinary tract infections, we show that full automation of prescribing fails to improve on physician decisions. Instead, optimally delegating a share of decisions to physicians, where they possess private diagnostic information, effectively utilizes the complementarity between algorithmic and human decisions. Combining physician and algorithmic decisions can achieve a reduction in inefficient overprescribing of antibiotics by 20.3 percent.
JEL-Classification: C53;D83;I18;I19;L2;M15
Keywords: Human-machine complementarity, Machine learning, Antibiotic resistance, Antibiotic prescribing
DOI:
https://doi.org/10.1007/s11129-024-09284-1