One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response
to the challenges related to AI. Transparency poses a key challenge for implementing AI …

Abstract Machine learning (ML) models are increasingly used for personnel assessment and
selection (eg, resume screeners, automatically scored interviews). However, concerns have …

Applying machine learning in real-world applications may have various implications on
companies, but individuals as well. Besides obtaining lower costs, faster time to decision …

This paper considers the need for generalizable bias mitigation techniques in machine
learning due to the growing concerns of fairness and discrimination in data-driven decision …

Recently, the usage of machine learning algorithms is subject to discussion from a legal and
ethical point of view. Unwanted discrimination regarding gender or race of a prediction …

In this paper, we propose FairML. jl, a Julia package providing a framework for fair
classification in machine learning. In this framework, the fair learning process is divided into …

In today's business, decision-making is heavily dependent on algorithms. Algorithms may
originate from operational research, machine learning, but also decision theory. Regardless …

The multiuser challenge within the field of Intelligent Environments, specifically concerning
Indoor Positioning systems needs to be addressed. Solving this challenge is paramount for …