Individually fair optimal decision trees

Individually fair optimal decision trees: Using a dynamic programming approach

KINDYNIS, Chrysanthos (TU Delft Electrical Engineering, Mathematics and Computer Science)

van der Linden, J.G.M. (mentor)
Demirović, E. (mentor)
Kulahcioglu Ozkan, Burcu (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-22

In this paper, we tackle the problem of creating decision trees that are both optimal and individually fair. While decision trees are popular due to their interpretability, achieving optimality can be difficult. Existing approaches either lack scalability or fail to consider individual fairness. To address this, we define individual fairness as a separable optimization task by analyzing the fairness gained and lost within a sub-tree. Using the Streed framework, we implement an algorithm that constructs optimal decision trees with the lowest misclassification score and individual fairness value above a certain threshold. Our algorithm has been tested on various datasets, demonstrating its effectiveness and scalability. This research is a significant step towards creating fair decision trees that are optimal, fair, and scalable.

Optimal Decision Tree
Individual Fairness
Dynamic Programming
Interpretable Machine Learning
Algorithms
Separability

http://resolver.tudelft.nl/uuid:556a8ad8-f17e-4142-966d-b4b996b5c0d9

Student theses

bachelor thesis

© 2023 Chrysanthos KINDYNIS