Addressing AI Bias Head-On: It's a Human Job

Artificial intelligence systems derive their power in learning to perform their tasks directly from data. As a result, AI systems are at the mercy of their training data and in most cases are strictly forbidden to learn anything beyond what is contained in their training data. Data by itself has some principal problems: It is noisy, nearly never complete, and it is dynamic as it continually changes over time. This noise can manifest in many ways in the data -- it can arise from incorrect labels, incomplete labels or misleading correlations. As a result of these problems with data, most AI systems must be very carefully taught how to make decisions, act or respond in the real world. This'careful teaching' involves three stages.