Work

Boundary-Based Fairness Constraints in Decision Trees and Random Forests

Year: 2021

Type: conference-paper

Abstract: Decision Trees (DTs) and Random Forests (RFs) are popular models in Machine Learning (ML) thanks to their interpretability and efficiency to solve real-world problems.However, DTs may sometimes learn rules that treat different groups of people unfairly, by paying attention to sensitive features like for example gender, age, income, language, etc.Even if several solutions have been proposed to reduce the unfairness for different ML algorithms, few of them apply to DTs.This work aims to transpose a successful method proposed by Zafar et al. [1] to reduce the unfairness in boundary based ML models to DTs.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2021.es2021-69

Authors: Géraldin Nanfack, Valentin Delchevalerie, Benoit Frénay

Institution: University of Namur

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funders: Waalse Gewest, Fonds De La Recherche Scientifique - FNRS, Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture, Université de Namur