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Bias Mitigation: Considerations
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Classroom Contents
Fairness in Representation Learning - Natalie Dullerud
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- 1 Fairness in Representation Learning A study in evaluation and mitigation of bias via subgroup
- 2 Fairness in Machine Learning
- 3 Fairness in Representations: DML
- 4 Overview: Fairness in Deep Metric Learning
- 5 Intuition: Fairness in DML
- 6 Defining Fairness in DML
- 7 Experimental Design
- 8 Empirical Results: Bias Propagates
- 9 Bias Mitigation: Considerations
- 10 Bias Mitigation: An Initial Solution (PARADE)
- 11 Empirical Results in PARADE
- 12 Comparison with Oversampling
- 13 Limitations to PARADE
- 14 Fairness Improvements in Representations
- 15 Thank you for listening!
- 16 PARtial Attribute DE-correlation (PARADE)
