Fairness issues, current approaches, and challenges in machine learning models
With the increasing influence of machine learning algorithms in decision-making processes,
concerns about fairness have gained significant attention. This area now offers significant …
concerns about fairness have gained significant attention. This area now offers significant …
On the applicability of machine learning fairness notions
Machine Learning (ML) based predictive systems are increasingly used to support decisions
with a critical impact on individuals' lives such as college admission, job hiring, child …
with a critical impact on individuals' lives such as college admission, job hiring, child …
Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms
Understanding and removing bias from the decisions made by machine learning models is
essential to avoid discrimination against unprivileged groups. Despite recent progress in …
essential to avoid discrimination against unprivileged groups. Despite recent progress in …
Machine learning fairness notions: Bridging the gap with real-world applications
Fairness emerged as an important requirement to guarantee that Machine Learning (ML)
predictive systems do not discriminate against specific individuals or entire sub-populations …
predictive systems do not discriminate against specific individuals or entire sub-populations …
Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods
TP Pagano, RB Loureiro, FVN Lisboa… - Big data and cognitive …, 2023 - mdpi.com
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 …
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …
Bias and unfairness in machine learning models: a systematic literature review
TP Pagano, RB Loureiro, FVN Lisboa… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …
Non-empirical problems in fair machine learning
T Scantamburlo - Ethics and Information Technology, 2021 - Springer
The problem of fair machine learning has drawn much attention over the last few years and
the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also …
the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also …
In-processing modeling techniques for machine learning fairness: A survey
Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …
clear benefits in terms of performance, the models could show discrimination against …
Impact of imputation strategies on fairness in machine learning
Abstract Research on Fairness and Bias Mitigation in Machine Learning often uses a set of
reference datasets for the design and evaluation of novel approaches or definitions. While …
reference datasets for the design and evaluation of novel approaches or definitions. While …
The Authority of" Fair" in Machine Learning
M Skirpan, M Gorelick - arXiv preprint arXiv:1706.09976, 2017 - arxiv.org
In this paper, we argue for the adoption of a normative definition of fairness within the
machine learning community. After characterizing this definition, we review the current …
machine learning community. After characterizing this definition, we review the current …