Open AccessPerformance analysis of machine learning algorithms trained on biased data
Author(s) -
Renata Sendreti Broder,
Lilian Berton
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2021.18283
Subject(s) - machine learning , artificial intelligence , computer science , decision tree , naive bayes classifier , random forest , ibm , support vector machine , harm , algorithm , id3 algorithm , statistical classification , decision tree learning , incremental decision tree , psychology , social psychology , materials science , nanotechnology
The use of Artificial Intelligence and Machine Learning algorithms in everyday life is common nowadays in several areas, bringing many possibilities and benefits to society. However, since there is room for learning algorithms to make decisions, the range of related ethical issues was also expanded. There are many complaints about Machine Learning applications that identify some kind of bias, disadvantaging or favoring some group, with the possibility of causing harm to a real person. The present work aims to shed light on the existence of biases, analyzing and comparing the behavior of different learning algorithms – namely Decision Tree, MLP, Naive Bayes, Random Forest, Logistic Regression and SVM – when being trained using biased data. We employed pre-processing algorithms for mitigating bias provided by IBM's framework AI Fairness 360.