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ML concerns
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Classroom Contents
Fairness in Machine Learning with Tulsee Doshi
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- 1 Introduction
- 2 ACM
- 3 Housekeeping
- 4 Overview
- 5 What is fairness
- 6 AI principles
- 7 ML example
- 8 ML concerns
- 9 Gender shades
- 10 Counterfactual Fairness
- 11 Equality of Opportunity
- 12 Improvements Mitigations
- 13 Recap
- 14 Transparency
- 15 Tools
- 16 Google Responsibility
- 17 Industrywide conversation
- 18 Questions and answers
- 19 Replicating the model
- 20 Bias in algorithms
- 21 ML fairness in sensor data
- 22 symmetric vs asymmetric data sets
- 23 subgroup analysis and fairness
- 24 closing remarks
