Debiasing judgmental decisions by providing individual error pattern feedback

We present a Decision Support System (DSS) that provides experts with feedback on their personal potential bias based on their previous error pattern. Feedback is calculated using a knowledge database containing a library of biases and typical error patterns that suggest them. An error pattern means any identifiable structure of errors. For instance, an inference engine might detect continuously too high forecasts of an expert submitted via a user interface, regularly exceeding the actual quantities observed later. The engine might then positively evaluate a rule indicating an overestimation bias and provide feedback on the detected error pattern and/or the presumed bias, potentially including further explanations. As the feedback stems from an expert’s own error pattern, it intends to enhance their self-reflection and support wise consideration of the feedback. We assume that this allows experts to acquire knowledge about their own flawed judgmental heuristics, that experts are able to apply the feedback systematically and selectively to different decision tasks and to therefore reduce their potential bias and error. To test these assumptions, we conduct experiments with the DSS. Therein, subjects provide point estimations as well as certainty intervals and subsequently receive error feedback given by a machine based on his or her previous answers. After the feedback, subjects answer further questions. Results indicate that subjects reflect on their own error pattern and apply the feedback selectively to further, upcoming estimations and reduce overall bias and error.