Responsible Machine Learning

Chapter 4. Technology: Engineering Machine Learning for Human Trust and Understanding

“If builders built houses the way programmers built programs, the first woodpecker to come along would destroy civilization.”

Gerald M. Weinberg

Human users of ML need to trust that any decision made by an ML system is maximally accurate, secure, and stable, and minimally discriminatory. We may also need to understand any decision made by an ML system for compliance, curiosity, debugging, appeal, or override purposes. This chapter discusses many technologies that can help organizations build human trust and understanding into their ML systems. We’ll begin by touching on reproducibility, because without that, you’ll never know if your ML system is any better or worse today than it was in the past. We’ll then proceed to interpretable models and post hoc explanation because interpretability into ML system mechanisms enables debugging of quality, discrimination, security, and privacy problems. After presenting some of these debugging techniques, we’ll close the chapter with a brief discussion of causality in ML.

Reproducibility

Establishing reproducible benchmarks to gauge improvements (or degradation) in accuracy, fairness, interpretability, privacy, or security is crucial for applying the scientific method. Reproducibility can also be necessary for regulatory compliance in certain cases. Unfortunately, the complexity of ML workflows makes reproducibility a real challenge. This section presents ...