Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints

Understand

Many applications of AI involve scoring individuals using a learned function of their attributes.

• These predictive risk scores are then used to take decisions based on whether the score exceeds a certain threshold, which may vary depending on the context.
• The level of delegation granted to such systems in critical applications like credit lending and medical diagnosis will heavily depend on how questions of fairness can be answered.
• In this paper, we study fairness for the problem of learning scoring functions from binary labeled data, a classic learning task known as bipartite ranking.