A General Framework for Fair Regression.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Fairness is a critical consideration in machine learning.
• Existing methods often struggle to incorporate fairness constraints effectively.
• Group fairness is a key aspect of ethical AI development.

Purpose of the Study:

• To develop a method for incorporating group fairness constraints into kernel regression.
• To analyze the impact of these constraints on decision tree regression models.
• To ensure the practical applicability of fairness-aware machine learning.

Main Methods:

• Kernel regression methods were adapted to include group fairness.
• Decision tree regression was specifically examined for fairness integration.
• Analysis focused on computational complexity and model perturbations.

Main Results:

• The incorporation of group fairness constraints preserves the computational complexity of kernel regression and decision tree models.
• Model perturbations are tightly bound by the number of leaves in decision trees.
• The proposed method is applicable to pre-trained models.

Conclusions:

• Group fairness can be effectively integrated into widely used machine learning models.
• The method offers a practical way to enhance the fairness of existing AI systems.
• Fairness-aware machine learning can be achieved without significant computational overhead.