TY - GEN
T1 - Boundary-Based Fairness Constraints in Decision Trees and Random Forests
AU - Nanfack, Geraldin
AU - Delchevalerie, Valentin
AU - Frénay, Benoît
N1 - Funding Information:
G.N. and V.D. were supported by the EOS VeriLearn project n. 30992574. V.D. is supported by the Walloon region with a Ph.D. grant from FRIA (F.R.S.-FNRS). This research used resources of PTCI at UNamur, supported by the F.R.S.-FNRS under the convention n. 2.5020.11. The authors thank Adrien Bibal for the fruitful discussions on this paper.
Publisher Copyright:
© 2021 ESANN Intelligence and Machine Learning. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85129302481&partnerID=8YFLogxK
U2 - 10.14428/esann/2021.ES2021-69
DO - 10.14428/esann/2021.ES2021-69
M3 - Conference contribution
T3 - ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
SP - 375
EP - 380
BT - ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
ER -
