Please Don't Turn Me Off: Optimizing AI Advice Timing for Human-AI Collaboration

Integrating AI assistance into decision-support systems has the promise of improving outcomes via human-AI complementarity effects. However, in practice, AI advice, particularly when unsolicited, is routinely turned off by people. To begin addressing this bottleneck in human-AI collaboration, we conducted an experiment in which people make decisions of variable difficulty with an AI assistant that automatically intervenes under one of three policies: offering advice on all trials, on a random subset of trials, or a complementary subset of trials. Since unprompted AI advice can be disruptive, participants were given the ability to turn AI advice off and back on at any time, with these on/off decisions serving as our primary behavioral measure. Our results show that advice timing is critical for maintaining human-AI collaboration, as advice perceived as irrelevant can prompt people to turn AI assistance off. This presents a critical risk, as participants were slow to re-enable AI advice even under conditions where advice was clearly beneficial. We construct a cognitive model to describe people's decisions to dis- and re-enable assistance and present a POMDP framework that integrates the cognitive model to estimate optimal, human-aware advice policies. This hybrid model is notable in that a policy estimation algorithm can consider how to avoid causing people to turn AI assistance off in the pursuit of human-AI complementarity by evaluating the counterfactual outcomes of offering AI advice on any given trial through the cognitive model.