Relevant paper: Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err [1] > We show that people are especially averse to algorithmic fo

Relevant paper: Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err [1] > We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. > [...] > The aversion to algorithms is costly, not only for the participants in our studies who lost money when they chose not to tie their bonuses to the algorithm, but for society at large. Many decisions require a forecast, and algorithms are almost always better forecasters than humans (Dawes, 1979; Grove et al., 2000; Meehl, 1954). The ubiquity of computers and the growth of the “Big Data” movement (Davenport & Harris, 2007) have encouraged the growth of algorithms but many remain resistant to using them. Our studies show that this resistance at least partially arises from greater intolerance for error from algorithms than from humans. People are more likely to abandon an algorithm than a human judge for making the same mistake. This is enormously problematic, as it is a barrier to adopting superior approaches to a wide range of important tasks. It means, for example, that people will more likely forgive an admissions committee than an admissions algorithm for making an error, even when, on average, the algorithm makes fewer such errors. In short, whenever prediction errors are likely—as they are in virtually all forecasting tasks—people will be biased against algorithms. 1: http://opim.wharton.upenn.edu/risk/library/WPAF201410-AlgorthimAversion-Dietvorst-Simmons-Massey.pdf http://opim.wharton.upenn.edu/risk/library/WPAF201410-Algort...