1 INTRODUCTION

2 FAIRNESS IN MACHINE LEARNING
2.1 Fairness Problem
2.2 Fairness Measurements
2.3 Survey Structure
2.4 Strategy for Paper Collection and Category Determination

3 EXPLICIT UNFAIRNESS MITIGATION
3.1 Disparate Impact Mitigation
3.2 Disparate Treatment Mitigation
3.3 Hybrid Methods for Explicit Bias Mitigation

4 IMPLICIT UNFAIRNESS MITIGATION
4.1 Impact-Driven Debiasing
4.2 Treatment-Driven Debiasing
4.3 Hybrid Methods for Implicit Mitigation

5 DISCUSSIONS

6 RESEARCH CHALLENGES

7 CONCLUSIONS