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Trusting the Algorithm: A Decision Under Ambiguity

Working Paper
The authors show that ambiguity attitudes influence decision-makers’ (DMs’) choices about whether to trust the forecasts of human and machinelearning (ML) financial analysts. DMs are similarly ambiguity-seeking and ambiguity-generated insensitive (“a-insensitive”; i.e., they insufficiently discriminate between changes in the likelihood of prediction accuracy) towards both analyst types. 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 choices. DMs’ ainsensitivity increases with financial literacy, suggesting that financially literate DMs perceive greater ambiguity in prediction accuracy.
Faculty

Professor of Decision Sciences