AI-Moderated Decision-Making: Capturing and Balancing Anchoring Bias in Sequential Decision Tasks

Decision-making involves biases from past experiences, which are difficult to perceive and eliminate. We investigate a specific type of anchoring bias, in which decision-makers are anchored by their own recent decisions, e.g. a college admission officer sequentially reviewing students. We propose an algorithm that identifies existing anchored decisions, reduces sequential dependencies to previous decisions, and mitigates decision inaccuracies post-hoc with 2% increased agreement to ground-truth on a large-scale college admission decision data set. A crowd-sourced study validates this algorithm on product preferences (5% increased agreement). To avoid biased decisions ex-ante, we propose a procedure that presents instances in an order that reduces anchoring bias in real-time. Tested in another crowd-sourced study, it reduces bias and increases agreement to ground-truth by 7%. Our work reinforces individuals with similar characteristics to be treated similarly, independent of when they were reviewed in the decision-making process.