Research shows that evidence-based algorithms more accurately predict the future than do human
forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical
algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion,
is costly, and it is important to understand its causes. We show that people are especially averse to
algorithmic forecasters after seeing them perform, even when they see them outperform a human
forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters
after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make
forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to
the future predictions of the algorithm or the human. Participants who saw the algorithm perform were
less confident in it, and less likely to choose it over an inferior human forecaster. This was true even
among those who saw the algorithm outperform the human.