TY - JOUR
T1 - Slow response times undermine trust in algorithmic (but not human) predictions
AU - Efendić, Emir
AU - van de Calseyde, P.P.F.M.
AU - Evans, Anthony
PY - 2020
Y1 - 2020
N2 - Algorithms consistently perform well on various prediction tasks, but people often mistrust their advice. Here, we demonstrate one component that affects people’s trust in algorithmic predictions: response time. In seven studies (total N = 1928 with 14,184 observations), we find that people judge slowly generated predictions from algorithms as less accurate and they are less willing to rely on them. This effect reverses for human predictions, where slowly generated predictions are judged to be more accurate. In explaining this asymmetry, we find that slower response times signal the exertion of effort for both humans and algorithms. However, the relationship between perceived effort and prediction quality differs for humans and algorithms. For humans, prediction tasks are seen as difficult and observing effort is therefore positively correlated with the perceived quality of predictions. For algorithms, however, prediction tasks are seen as easy and effort is therefore uncorrelated to the quality of algorithmic predictions. These results underscore the complex processes and dynamics underlying people’s trust in algorithmic (and human) predictions and the cues that people use to evaluate their quality.
AB - Algorithms consistently perform well on various prediction tasks, but people often mistrust their advice. Here, we demonstrate one component that affects people’s trust in algorithmic predictions: response time. In seven studies (total N = 1928 with 14,184 observations), we find that people judge slowly generated predictions from algorithms as less accurate and they are less willing to rely on them. This effect reverses for human predictions, where slowly generated predictions are judged to be more accurate. In explaining this asymmetry, we find that slower response times signal the exertion of effort for both humans and algorithms. However, the relationship between perceived effort and prediction quality differs for humans and algorithms. For humans, prediction tasks are seen as difficult and observing effort is therefore positively correlated with the perceived quality of predictions. For algorithms, however, prediction tasks are seen as easy and effort is therefore uncorrelated to the quality of algorithmic predictions. These results underscore the complex processes and dynamics underlying people’s trust in algorithmic (and human) predictions and the cues that people use to evaluate their quality.
KW - Algorithm aversion
KW - DECISION TIME
KW - Human-computer interaction
KW - JUDGMENT
KW - Judgment and decision making
KW - PEOPLE
KW - Prediction
KW - Response time
UR - http://www.scopus.com/inward/record.url?scp=85079058427&partnerID=8YFLogxK
U2 - 10.1016/j.obhdp.2020.01.008
DO - 10.1016/j.obhdp.2020.01.008
M3 - Article
SN - 0749-5978
VL - 157
SP - 103
EP - 114
JO - Organizational Behavior and Human Decision Processes
JF - Organizational Behavior and Human Decision Processes
ER -