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Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods
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 …
A review on fairness in machine learning
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
Trustworthy artificial intelligence: a review
Artificial intelligence (AI) and algorithmic decision making are having a profound impact on
our daily lives. These systems are vastly used in different high-stakes applications like …
our daily lives. These systems are vastly used in different high-stakes applications like …
A call to action on assessing and mitigating bias in artificial intelligence applications for mental health
Advances in computer science and data-analytic methods are driving a new era in mental
health research and application. Artificial intelligence (AI) technologies hold the potential to …
health research and application. Artificial intelligence (AI) technologies hold the potential to …
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 …
Fairness in recommendation: Foundations, methods, and applications
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …
playing an important role on assisting human decision-making. The satisfaction of users and …
A sociotechnical view of algorithmic fairness
Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates
systemic discrimination in automated decision‐making, providing opportunities to improve …
systemic discrimination in automated decision‐making, providing opportunities to improve …
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …
related fields. This review asks the question: How can a classifier learn from a source …
Fairness constraints: A flexible approach for fair classification
Algorithmic decision making is employed in an increasing number of real-world applications
to aid human decision making. While it has shown considerable promise in terms of …
to aid human decision making. While it has shown considerable promise in terms of …