• Machine Learning and Human Bias

  1. Based on data (Not automatically make it neutral)
  2. Biases - Part of the technology we create in many different ways

• Evaluating Metrics for Inclusion

• Statistical Measurements and acceptable tradeoffs

  1. Reiterate metrics
  2. Evaluation metrics

Key things to measure how inclusive a machine learning system is

• Equality of Opportunity

• Simulating Decisions
  Restrict equal opportunity thresholds to invest in the best classifiers

• Finding Errors in your dataset using Facets

Summary