Impact of Model Ensemble on the Fairness of Classifiers in Machine Learning

Patrik Joslin Kenfack, Adil Mehmood Khan, S. M. Ahsan Kazmi, Rasheed Hussain, Alma Oracevic, Asad Masood Khattak

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

12 Citations (Scopus)

Abstract

Machine Learning (ML) models are trained using historical data that may contain stereotypes of the society (biases). These biases will be inherently learned by the ML models which might eventually result in discrimination against certain subjects, for instance, people with certain protected characteristics (race, gender, age, religion, etc.). Since the decision provided by ML models might affect people's lives, fairness of these models becomes crucially important. When training a model with fairness constraints, a significant loss in accuracy relative to the unconstrained model may be unavoidable. Reducing the trade-off between fairness and accuracy is an active research question within the fair ML community, i.e., to provide models with high accuracy with as little bias as possible. In this paper, we extensively investigate the fairness metrics over different ML models and study the impact of ensemble models on fairness. To this end, we compare different ensemble strategies and empirically show which strategy is preferable for different fairness metrics. Furthermore, we also propose a novel weighting technique that allows a balance between fairness and accuracy. In essence, we assign weights such that they are proportional to classifiers' performance in term of fairness and accuracy. Our experimental results show that our weighting technique reduces the trade-off between fairness and accuracy in ensemble models.

Original languageEnglish
Title of host publication2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781728159348
DOIs
Publication statusPublished - 19 May 2021
Event2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021 - Halden, Norway
Duration: 19 May 202121 May 2021

Publication series

Name2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021

Conference

Conference2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021
Country/TerritoryNorway
CityHalden
Period19/05/2121/05/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Biases
  • Ensemble models
  • Explainable AI
  • Fairness
  • Machine Learning

Fingerprint

Dive into the research topics of 'Impact of Model Ensemble on the Fairness of Classifiers in Machine Learning'. Together they form a unique fingerprint.

Cite this