The rise of machine learning has fundamentally altered decision making: rather than being made solely by people, many important decisions are now made through an “algorithm-in-the-loop” process where machine learning models inform people. Yet insufficient research has considered how the interactions between people and models actually influence human decision making. In this talk, I’ll discuss results from a set of controlled experiments on algorithm-in-the-loop human decision making in two contexts (pretrial release and financial lending). For example, when presented with algorithmic risk assessments, our study participants exhibited additional bias in their decisions and showed a change in their decision-making process by increasing risk aversion. These results highlight the urgent need to expand our analyses of algorithmic decision making aids beyond evaluating the models themselves to investigating the full sociotechnical contexts in which people and algorithms interact.