The original title is 10 words: "Your AI Isn't Racist, It Just Read a Lot of Bad History"

Short summary

Machine learning models inherit bias from training data through imbalanced examples and proxy variables, not explicit programming. Techniques like rebalancing data, adjusting model training, or post-processing outputs can reduce fairness problems, but each involves trade-offs between overall accuracy and fair distribution of errors. Testing for discrimination requires handling sensitive data responsibly with legal safeguards and documented justification.

• •ML models inherit bias from training data through imbalanced examples and proxy variables, not deliberate programming
• •Fairness techniques (pre-processing, in-processing, post-processing) each involve trade-offs; no single approach satisfies all fairness definitions
• •Responsible bias testing requires handling sensitive data with proper legal safeguards and documented justification

Generated with AI, which can make mistakes.