People lose confidence more quickly in algorithms than in humans after seeing the same kind of mistakes¶

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? Seeing the model perform significantly decreased confidence in the model's forecasts in all four relevant studies, whereas seeing the human perform significantly decreased confidence in the human only once. → 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

Confidence ratings (5-point scales) collected after the choice across Studies 1, 3a, 3b, and 4 showed the asymmetric pattern; e.g., in Study 1, seeing the model perform lowered confidence in the model significantly while seeing the human perform did not (p = .262).

"Whereas seeing the human perform did not consistently decrease confidence in the human’s forecasts—it did so significantly only in Study 4, seeing the model perform significantly decreased participants’ confidence in the model’s forecasts in all four studies."

Discussion¶

Related Claims¶

• Algorithm aversion persists even among participants who saw the algorithm outperform the human forecaster — related
• Confidence in the algorithm's forecasts mediates the effect of seeing it perform on choosing it — 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