Confidence in the algorithm's forecasts mediates the effect of seeing it perform on choosing it¶

Claim · All claims
Evidence · 1 study · causal r2 · q3
Currency · newest study 2014 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Binary mediation analyses showed confidence in the model's forecasts, but not confidence in the human's forecasts, mediated the effect of seeing the model perform on choosing the model. → Berkeley J. Dietvorst 2014

Evidence¶

Berkeley J. Dietvorst 2014¶

Berkeley J. Dietvorst, Joseph P. Simmons, and Cade Massey. (2014). Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err. Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0000033

q3 · i? · causal · r2

Preacher and Hayes bootstrapping analyses in each study yielded significant confidence intervals for confidence in the model's forecasts (e.g., Study 1, 95% CI [–.165, –.070]) and nonsignificant intervals for confidence in the human's forecasts.

"In all cases, confidence in the model’s forecasts significantly mediated the effect, whereas confidence in the human did not."

Discussion¶

Related Claims¶

• Algorithm aversion persists even among participants who saw the algorithm outperform the human forecaster — related
• People lose confidence more quickly in algorithms than in humans after seeing the same kind of mistakes — related
• Seeing an algorithm perform makes people significantly less likely to choose it over a human forecaster — related
• A belief that the algorithm is less likely to be perfect does not explain experience-driven algorithm aversion — related
• Slight control over an algorithm's output reduces algorithm aversion, with process control producing the strongest calibration effects — related