Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness

Understand

Much of machine learning research focuses on predictive accuracy: given a task, create a machine learning model (or algorithm) that maximizes accuracy.

• In many settings, however, the final prediction or decision of a system is under the control of a human, who uses an algorithm's output along with their own personal expertise in order to produce a combined prediction.
• One ultimate goal of such collaborative systems is "complementarity": that is, to produce lower loss (equivalently, greater payoff or utility) than either the human or algorithm alone.
• However, experimental results have shown that even in carefully-designed systems, complementary performance can be elusive.