Practical strategies to minimize bias in machine learning

We've been seeing the headlines for years: "Researchers find flaws in the algorithms used…" for nearly every use case for AI, including finance, health care, education, policing, or object identification. Most conclude that if the algorithm had only used the right data, was well vetted, or was trained to minimize drift over time, then the bias never would have happened. There are several practical strategies that you can adopt to instrument, monitor, and mitigate bias through a disparate impact measure. For models that are used in production today, you can start by instrumenting and baselining the impact live. For analysis or models used in one-time or periodic decision making, you'll benefit from all strategies except for live impact monitoring.