Enhancing Fairness In Supervised Machine Learning
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Enhancing Fairness in Supervised Machine Learning
The increasing influence of machine learning algorithms and artificial intelligence on the high-impact domains of decision-making has led to an increasing concern for the ethical and legal challenges posed by sensitive data-driven systems. Machine learning can identify the statistical patterns in the historically collected big data generated by a huge number of instances that might be affected by human and structural biases. ML algorithms have the potential to amplify these inequities. Lately, there have been several attempts to reduce bias in artificial intelligence in order to maintain fairness in machine learning projects. These methods fall under three categories of pre-processing, in-processing, and post-processing techniques. There are at least 21 notations of fairness in the recent literature, which not only provide different measurement methods of fairness but also lead to completely different concepts. It is worth mentioning that, it is impossible to satisfy all of the definitions of fairness at the same time and some of them are incompatible with each other. As a result, it is important to choose a fairness definition that need to be satisfied according to the context that we are working on. The current study investigates some of the most common definitions and metrics for fairness introduced by researchers to compare three of the proposed de-biasing techniques regarding their effects on the performance and fairness measures through empirical experiments on four different datasets. The de-biasing methods include the "Reweighing Algorithm", "Adversarial De-biasing Method", the "Reject Option Classification Method" performed on the classification tasks of "Survival of patients with heart failure"(Heart Failure Dataset), "Prediction of hospital readmission among diabetes patients" (Diabetes Dataset), "Credit classification of bank account holders" (German Credit Dataset), and "The COVID19 related anxiety level classification of Canadians" (CAMH Dataset). Findings show that the adversarial de-biasing in-processing method can be the best technique for mitigating bias working with the deep learning classifiers when we are capable of changing the classification process. This method has not led to a considerable reduction of accuracy except for the CAMH dataset. The "Reject Option Classification" which is a post-processing method, causes the most deterioration of prediction accuracy in all datasets. On the other hand, this method has the best performance in alleviating the bias generated through the classification process. The "Reweighing Algorithm" as a pre-processing technique does not cause a considerable reduction in the accuracy and is capable of reducing bias in classification tasks, although its performance is not as strong as the Reject Option classifier.
Pattern Recognition
Author: Apostolos Antonacopoulos
language: en
Publisher: Springer Nature
Release Date: 2024-12-03
The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics.
Machine Learning Algorithms for Enhancing Cyber Defense Systems
Author: Arokia Suresh Kumar Joseph
language: en
Publisher: RK Publication
Release Date: 2024-11-19
Machine Learning Algorithms for Enhancing Cyber Defense Systems the transformative role of machine learning in fortifying cybersecurity frameworks. The provides a comprehensive analysis of key algorithms, their applications in threat detection, anomaly analysis, and automated response, and their ability to adapt to evolving cyber threats. With an emphasis on real-world case studies and emerging trends, it bridges the gap between theoretical concepts and practical implementations, offering insights for professionals, researchers, and students aiming to harness machine learning for robust and intelligent cyber defense.