Facing the Challenges of Developing Fair Risk Scoring Models

Area of Science:

• Machine Learning
• Financial Risk Management
• Causal Inference

Background:

• Algorithmic scoring is prevalent in finance for risk management and decision optimization.
• Regulatory demands (e.g., BCBS, EU data protection) drive interest in explainable AI.
• Current methods, including explainable AI, do not guarantee fairness, as models can exhibit bias against subpopulations.

Purpose of the Study:

• To present a framework for analyzing and developing fair machine learning models.
• To define and implement counterfactual fairness in algorithmic scoring.
• To investigate the trade-off between fairness and predictive accuracy.

Main Methods:

• Utilizing causal inference techniques combined with explainable machine learning methods.
• Developing an algorithm for fair scoring models based on counterfactual fairness.
• Applying the methodology to a transparent simulation and the German Credit dataset.

Main Results:

• A framework for analyzing and developing fair scoring models is presented.
• Counterfactual fairness is defined and operationalized with an algorithm.
• The study shows that unfairness can be mitigated with minimal impact on predictive accuracy, provided discriminative attributes are not overly correlated with other predictors.

Conclusions:

• It is feasible to create fair algorithmic scoring models using causal inference.
• The trade-off between fairness and accuracy is manageable in many scenarios.
• Explaining fairness implications of these models to users remains a key challenge.