The Effect of Interpretable Artificial Intelligence on Repeated Managerial Decision-Making Under Uncertainty

Many business decisions, such as managing investment instruments, selecting medical care, and managing various supply chains, are being made repetitively in uncertain environments. At times, decisions under uncertainty could be perceived as wrong ex-post even though they are really ex-ante optimal. Therefore, decision-makers tend to intuitively resist taking algorithmic advice under high uncertainty. While recent studies claim that interpretability potentially increases the adoption of and trust in sophisticated AI and ML applications, these claims have not been tested with humans who must make repeated managerial decisions under uncertainty. Our study fills this gap by empirically investigating the impact of various common interpretability formats on AI adoption and trust under uncertainty. Interestingly, we show that explaining the inner workings of a model, in fact, decreases AI adoption. This is probably due to what we call 'computational overconfidence' on the part of the users. Moreover, contrary to other findings in the literature, we present clear evidence that AI adoption is significantly higher in more-uncertain business environments. Surprisingly, although AI adoption was greater in such environments, the level of trust in AI was considerably lower for all the interpretability types tested in this study. On the other hand, frequently sharing the cumulative performance over time of both the users and the AI improves users’ trust in the model. On the other hand, it had no negative impact on the AI adoption rate