  AI practitioners typically strive to develop the most accurate systems,
making an implicit assumption that the AI system will function autonomously.
However, in practice, AI systems often are used to provide advice to people in
domains ranging from criminal justice and finance to healthcare. In such
AI-advised decision making, humans and machines form a team, where the human is
responsible for making final decisions. But is the most accurate AI the best
teammate? We argue "No" -- predictable performance may be worth a slight
sacrifice in AI accuracy. Instead, we argue that AI systems should be trained
in a human-centered manner, directly optimized for team performance. We study
this proposal for a specific type of human-AI teaming, where the human overseer
chooses to either accept the AI recommendation or solve the task themselves. To
optimize the team performance for this setting we maximize the team's expected
utility, expressed in terms of the quality of the final decision, cost of
verifying, and individual accuracies of people and machines. Our experiments
with linear and non-linear models on real-world, high-stakes datasets show that
the most accuracy AI may not lead to highest team performance and show the
benefit of modeling teamwork during training through improvements in expected
team utility across datasets, considering parameters such as human skill and
the cost of mistakes. We discuss the shortcoming of current optimization
approaches beyond well-studied loss functions such as log-loss, and encourage
future work on AI optimization problems motivated by human-AI collaboration.
