Fairness in Machine Learning


Machine Learning fairness is directly related to almost all fields where Machine Learning can be applied:

  • Autonomous machines
  • Job application workflow
  • Predictive models for the justice system
  • Online shopping recommendation systems
  • etc.

Many of the causes in ML unfairness or bias can be tracked to the original training data. Some common causes include:

  • Skewed observations
  • Tainted observations
  • Limited features
  • Sample size disparity
  • Proxies

Some algortihms discussed in these pages: