Algorithm aversion persists even among participants who saw the algorithm outperform the human forecaster¶

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? Participants in the model-and-human condition who saw the model beat the human were still less likely to choose the model than participants who had not seen the model perform. → 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

Chi-square tests across Studies 1, 3a, 3b, and 4 restricted to model-and-human participants who saw the model outperform the human (83%, or 610 of 741, across the five studies) confirmed the aversion; Study 3a's test was not significant in that subsample.

"This result is not limited to the minority who saw the human outperform the model, as even those who saw the model outperform the human were less likely to choose the model than were participants who did not see the model perform."

Discussion¶

Related Claims¶

• Seeing an algorithm perform makes people significantly less likely to choose it over a human forecaster — a broader claim this one bears on
• Confidence in the algorithm's forecasts mediates the effect of seeing it perform on choosing it — related
• People lose confidence more quickly in algorithms than in humans after seeing the same kind of mistakes — 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