Rethinking Safe Control in the Presence of Self-Seeking Humans

Safe control methods are often intended to behave safely even in worst-case human uncertainties. However, humans may exploit such safety-first systems, which results in greater risk for everyone. Despite their significance, no prior work has investigated and accounted for such factors in safe control. In this paper, we leverage an interaction-based payoff structure from game theory to model humans' short-sighted, self-seeking behaviors and how humans change their strategies toward machines based on prior experience. We integrate such strategic human behaviors into a safe control architecture. As a result, our approach achieves better safety and performance trade-offs when compared to both deterministic worst-case safe control techniques and equilibrium-based stochastic methods. Our findings suggest an urgent need to fundamentally rethink the safe control framework used in human-technology interaction in pursuit of greater safety for all.