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Friday, February 28, 2020

Slow response times undermine trust in algorithmic (but not human) predictions

E Efendic, P van de Calseyde, & A Evans
PsyArXiv PrePrints
Lasted Edited 22 Jan 20


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 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.

General discussion 

When are people reluctant to trust algorithm-generated advice? Here, we demonstrate that it depends on the algorithm’s response time. People judged slowly (vs. quickly) generated predictions by algorithms as being of lower quality. Further, people were less willing to use slowly generated algorithmic predictions. For human predictions, we found the opposite: people judged slow human-generated predictions as being of higher quality. Similarly, they were more likely to use slowly generated human predictions. 

We find that the asymmetric effects of response time can be explained by different expectations of task difficulty for humans vs. algorithms. For humans, slower responses were congruent with expectations; the prediction task was presumably difficult so slower responses, and more effort, led people to conclude that the predictions were high quality. For algorithms, slower responses were incongruent with expectations; the prediction task was presumably easy so slower speeds, and more effort, were unrelated to prediction quality. 

The research is here.