Fairness optimisation with multi-objective swarms for explainable classifiers on data streams

被引:1
作者
Pham, Diem [1 ,2 ]
Tran, Binh [1 ]
Nguyen, Su [3 ]
Alahakoon, Damminda [1 ]
Zhang, Mengjie [4 ]
机构
[1] La Trobe Univ, Melbourne, Vic 3086, Australia
[2] Can Tho Univ, Can Tho 900000, Vietnam
[3] RMIT Univ, Melbourne, Vic 3001, Australia
[4] Victoria Univ Wellington, Wellington 6140, New Zealand
关键词
Fairness; Explainable; Multi-objective; Data streams; Swarm intelligence; CLASSIFICATION;
D O I
10.1007/s40747-024-01347-w
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, advanced AI systems equipped with sophisticated learning algorithms have emerged, enabling the processing of extensive streaming data for online decision-making in diverse domains. However, the widespread deployment of these systems has prompted concerns regarding potential ethical issues, particularly the risk of discrimination that can adversely impact certain community groups. This issue has been proven to be challenging to address in the context of streaming data, where data distribution can change over time, including changes in the level of discrimination within the data. In addition, transparent models like decision trees are favoured in such applications because they illustrate the decision-making process. However, it is essential to keep the models compact because the explainability of large models can diminish. Existing methods usually mitigate discrimination at the cost of accuracy. Accuracy and discrimination, therefore, can be considered conflicting objectives. Current methods are still limited in controlling the trade-off between these conflicting objectives. This paper proposes a method that can incrementally learn classification models from streaming data and automatically adjust the learnt models to balance multi-objectives simultaneously. The novelty of this research is to propose a multi-objective algorithm to maximise accuracy, minimise discrimination and model size simultaneously based on swarm intelligence. Experimental results using six real-world datasets show that the proposed algorithm can evolve fairer and simpler classifiers while maintaining competitive accuracy compared to existing state-of-the-art methods tailored for streaming data.
引用
收藏
页码:4741 / 4754
页数:14
相关论文
共 38 条
  • [1] [Anonymous], 2006, International Journal of Computational Intelligence Research, DOI DOI 10.5019/J.IJCIR.2006.68
  • [2] jMetalPy: A Python']Python framework for multi-objective optimization with metaheuristics
    Benitez-Hidalgo, Antonio
    Nebro, Antonio J.
    Garcia-Nieto, Jose
    Oregi, Izaskun
    Del Ser, Javier
    [J]. SWARM AND EVOLUTIONARY COMPUTATION, 2019, 51
  • [3] Bifet A, 2010, JMLR WORKSH CONF PRO, V11, P44
  • [4] Bifet A, 2010, LECT NOTES ARTIF INT, V6332, P1, DOI 10.1007/978-3-642-16184-1_1
  • [5] Bifet A, 2007, PROCEEDINGS OF THE SEVENTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, P443
  • [6] Brzezinski D, 2011, LECT NOTES ARTIF INT, V6679, P155, DOI 10.1007/978-3-642-21222-2_19
  • [7] Three naive Bayes approaches for discrimination-free classification
    Calders, Toon
    Verwer, Sicco
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2010, 21 (02) : 277 - 292
  • [8] Caldwell Tracey, 2009, Information World Review, P13
  • [9] Deb K., 2011, Multi-objective Manufacturing, P3, DOI DOI 10.1007/978-0-85729-652-81
  • [10] Domingos P., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P71, DOI 10.1145/347090.347107