In this PhD thesis the abstract reads
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. In Chapter 1, 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 Chapter 2, we investigate how aversion to imperfect algorithms might be overcome. We find that people are considerably more likely to choose to use an imperfect algorithm, and thus perform better, when they can modify its forecasts. Importantly, this is true even when they are severely restricted in the modifications they can make. Moreover, we find that people’s decision to use a modifiable algorithm is relatively insensitive to the magnitude of the modifications they are able to make. Finally, we find that giving people the freedom to modify an imperfect algorithm makes them feel more satisfied with the forecasting process, more likely to believe that the algorithm is superior, and more likely to choose to use an algorithm to make subsequent forecasts
A slim link to the idea of protean digital technology.
Implications for learning analytics.