Evaluating Algorithm-Assisted Human Decision-Making Over Repeated Algorithm Exposure: Recommendations for Effect Estimands and Experimental Design

Abstract

In algorithm-assisted decision-making in high-stakes settings like healthcare, an algorithmic decision support tool provides a recommendation, but the human ultimately makes the decision. Determining whether algorithm assistance actually improves the quality of human decision-making prior to deployment is critical, and randomized experiments are one way to collect robust evidence. Historically, however, experimental designs and analyses ignore how decision-making behavior adapts with repeated algorithm exposure. In this work, we define a set of effect estimands that account for and characterize human behavior adaptation under repeated exposure and justify why these estimands are useful to target for developing a better understanding of the impact of algorithm assistance. We propose a minimax stepped double wedge design that facilitates estimating these target estimands. Finally, we compare our proposed design to two common alternative designs identified through a review of historical randomized trials of algorithm assistance. We show that these designs are less amenable to estimating the target estimands and produce biased estimates under three different forms of behavioral adaptation inspired by dynamics observed in the real world -- automation bias, alert fatigue, and calibrated reliance.