An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision

Understand

The notion of individual fairness requires that similar people receive similar treatment.

• However, this is hard to achieve in practice since it is difficult to specify the appropriate similarity metric.
• In this work, we attempt to learn such similarity metric from human annotated data.
• We gather a new dataset of human judgments on a criminal recidivism prediction (COMPAS) task.