Trusting Human Versus Machine Predictions as a Decision Under Ambiguity

The authors examine how decision-makers’ (DMs’) ambiguity attitudes shape trust for two different sources of financial forecasting: human or machine learning (ML). In an incentivized laboratory experiment, they measure participants’ ambiguity attitudes and optimism regarding forecast accuracy for both sources. Their results reveal that DMs are similarly ambiguity-seeking and ambiguity-generated insensitive (“a-insensitive”; i.e., they insufficiently discriminate between changes in the likelihood of prediction accuracy), regardless of the analyst type. DMs hold more optimistic beliefs about the accuracy of ML analysts, which predicts higher trust in ML analysts over human analysts. However, DMs who are more a-insensitive are less likely to incorporate their beliefs into their trust. DMs’ a-insensitivity increases with financial literacy, suggesting that financially literate DMs perceive greater ambiguity in prediction accuracy. Their findings demonstrate that a-insensitivity acts as a cognitive barrier between beliefs and trust.