Bias mitigation for machine learning classifiers: A comprehensive survey
This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …
A survey on datasets for fairness‐aware machine learning
As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …
A survey on bias and fairness in machine learning
With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …
everyday lives, accounting for fairness has gained significant importance in designing and …
Trustworthy ai: A computational perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …
developments, changing everyone's daily life and profoundly altering the course of human …
[HTML][HTML] Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods
B Van Giffen, D Herhausen, T Fahse - Journal of Business Research, 2022 - Elsevier
Over the last decade, the importance of machine learning increased dramatically in
business and marketing. However, when machine learning is used for decision-making, bias …
business and marketing. However, when machine learning is used for decision-making, bias …
Artificial intelligence and the public sector—applications and challenges
BW Wirtz, JC Weyerer, C Geyer - International Journal of Public …, 2019 - Taylor & Francis
Advances in artificial intelligence (AI) have attracted great attention from researchers and
practitioners and have opened up a broad range of beneficial opportunities for AI usage in …
practitioners and have opened up a broad range of beneficial opportunities for AI usage in …
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse
AL Hoffmann - Information, Communication & Society, 2019 - Taylor & Francis
Problems of bias and fairness are central to data justice, as they speak directly to the threat
that 'big data'and algorithmic decision-making may worsen already existing injustices. In the …
that 'big data'and algorithmic decision-making may worsen already existing injustices. In the …
[PDF][PDF] A framework for understanding unintended consequences of machine learning
As machine learning increasingly affects people and society, it is important that we strive for
a comprehensive and unified understanding of how and why unwanted consequences …
a comprehensive and unified understanding of how and why unwanted consequences …
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
The most prevalent notions of fairness in machine learning fix a small collection of pre-
defined groups (such as race or gender), and then ask for approximate parity of some …
defined groups (such as race or gender), and then ask for approximate parity of some …