Should We Teach Machine Learning Systems?

When we find that AI-powered systems behave unexpectedly or inappropriately, we often discuss how we can retrain and find or generate better or unbiased data. This is because we have a general hands-off perspective on learning: we expect a system to build a better model if the learning process is left to make sense of input data with minimal supervision and guidance.

This talk will discuss the place of feature engineering and instruction in machine learning to mitigate some of the observed challenges of providing users with high-quality and reliable output and contrast it with today's approach.