Emergent behavior arising in a joint human-robot system cannot be fully predicted based on an understanding of the individual agents. Typically, robot behavior is governed by algorithms that optimize a reward function that should quantitatively capture the joint system's goal. Although reward functions can be updated to better match human needs, this is no guarantee that no misalignment with the complex and variable human needs will occur. Algorithms may learn undesirable behavior when interacting with the human and the intrinsically unpredictable human-inhabited world, thereby producing further misalignment with human users or bystanders. As a result, humans might behave differently than anticipated, causing robots to learn differently and undesirable behavior to emerge. With this short paper, we state that to design for Human-Robot Interaction that mitigates such undesirable emergent behavior, we need to complement advancements in human-robot interaction algorithms with human factors knowledge and expertise. More specifically, we advocate a three-pronged approach that we illustrate using a particularly challenging example of safety-critical human-robot interaction: a driver interacting with a semi-automated vehicle. Undesirable emergent behavior should be mitigated by a combination of 1) including driver behavioral mechanisms in the vehicle's algorithms and reward functions, 2) model-based approaches that account for interaction-induced driver behavioral adaptations and 3) driver-centered interaction design that promotes driver engagement with the semi-automated vehicle, and the transparent communication of each agent's actions that allows mutual support and adaptation. We provide examples from recent empirical work in our group, in the hope this proves to be fruitful for discussing emergent human-robot interaction.