Seven Principles of Building Fair Machine Learning Systems

We’ve all heard examples of unfair AI. Job ads targeting people similar to current employees drive only young men to recruiter inboxes. Cancer detection systems that don’t work as well on darker skin. When building these machine learning (ML) models, we need to do better at removing bias, not only for compliance and ethical reasons but also because fair systems earn trust, and trusted companies perform better.

About George Veth

George Veth is a consultant in the areas of strategy execution and initiative management. Most recently, he has been leading a cross-boundary collaboration program with teams from cities across North America and Europe. He lives in Cambridge, MA, and runs a nonprofit SME Impact Fund in East Africa. His subject matter interests are organizational culture, management [system] innovation, and public value management.