Evaluating Algorithm-assisted Human Decision-making Over Repeated Algorithm Exposure

Thomas M. Menino Convention & Exhibition Center 

In many forms of algorithm-assisted decision-making, an algorithmic decision support tool provides a recommendation, but the human ultimately makes the decision. Determining whether algorithm assistance actually improves human decision-making 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 demonstrate how using a per-decision randomized design and estimating an average effect across all decisions results in misleading effect estimates under three forms of adaptation: gradual overreliance on the algorithm, gradual ignoring of the algorithm, and gradual learning of where the algorithm makes mistakes. We then propose using a staggered rollout design to target alternative effect estimands: the global effect, the habituation effect, and the immediate effect. Finally, drawing on the concept of local average treatment effects, we show that we can identify habituated and immediate effects specifically for decisions where the decision-maker is persuaded by the algorithm to change their decision.

experimental design

time-varying treatment effects

human-algorithm collaboration 

Section on Statistical Learning and Data Science