With the development of edge devices and cloud computing, the question of how to
accomplish machine learning and optimization tasks in a privacy-preserving and secure way …

Machine learning algorithms significantly impact decision-making in high-stakes domains,
necessitating a balance between fairness and accuracy. This study introduces an in …

Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training
fairer machine learning models considering a predefined set of conflicting objectives …

The construction of machine learning models involves many bi-level multiobjective
optimization problems (BL-MOPs), where upper-level (UL) candidate solutions must be …

In the era of data-driven decision-making, ensuring fairness and equality in machine
learning models has become increasingly crucial. Multiple fairness definitions have been …

This paper introduces a novel approach, evolutionary multi-objective optimisation for
fairness-aware self-adjusting memory classifiers, designed to enhance fairness in machine …

Given the growing concerns about bias in machine learning, dozens of metrics have been
proposed to measure the fairness of machine learning. Several platforms have also been …

The financial sector is increasingly leveraging Artificial Intelligence (AI) to deliver intelligent,
automated, and personalized services. However, it encounters significant data privacy …

Bias in data may lead to prediction procedures which discriminate individuals from sensitive
groups. In this paper we propose a Bayesian method for parameter estimation in the linear …