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Intro
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Classroom Contents
Algorithmic Decision Making and the Cost of Fairness
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- 1 Intro
- 2 How do we identify bias in algorithmic decisions?
- 3 Case study: Pre-trial decision making
- 4 Problems with the benchmark test
- 5 The outcome test in Broward County
- 6 Risk distributions
- 7 The problem with the outcome test
- 8 The problem of infra-marginality
- 9 Identifying bias in human decisions
- 10 Making decisions with algorithms
- 11 Evidence from Broward County
- 12 Potential fairness concerns
- 13 Redlining
- 14 Why is calibration insufficient?
- 15 Sample bias
- 16 Label bias
- 17 Subgroup validity
- 18 Use of protected characteristics
- 19 Statistical parity as a measure of fairness
- 20 Where do these disparities come from?
- 21 The optimal rule is a single threshold
- 22 The fairness/fairness trade-off
- 23 Analogies to tests for discrimination
- 24 The problem with false positive rates
- 25 Making fair decisions with algorithms
- 26 Limitations
