Intelligence, trust & financial decisions · CC BY 4.0
The extent of algorithm aversion in decision-making situations with varying gravity
Ibrahim Filiz; Jan René Judek; Marco Lorenz; Markus Spiwoks
PLOS ONE · 2023-02-21
Adjacent evidence for family-office oversight and financial decisions; no directly studied family-office sample. Warranted reliance on algorithms: make performance and consequences visible
Original practical companion
Warranted reliance on algorithms: make performance and consequences visible
AI-assisted editorial analysis. The source reports an experiment with 143 students at a German university. Participants chose between a human expert and an algorithm in one of six framed service scenarios. The algorithm was assigned a seventy percent success probability and the human expert sixty percent, with the same monetary incentive across scenarios. Participants were less likely to select the algorithm in scenarios perceived as more serious. This provides a useful question for family-office technology governance: does the perceived gravity of a task alter willingness to use a tool even when its performance information is held constant?