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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 systematic review of fairness in machine learning
Abstract Fairness in Machine Learning (ML) has emerged as a crucial concern as these
models increasingly influence critical decisions in various domains, including healthcare …
models increasingly influence critical decisions in various domains, including healthcare …
Fairness without demographic data: A survey of approaches
Detecting, measuring and mitigating various measures of unfairness are core aims of
algorithmic fairness research. However, the most prominent approaches require access to …
algorithmic fairness research. However, the most prominent approaches require access to …
When less is enough: Positive and unlabeled learning model for vulnerability detection
Automated code vulnerability detection has gained increasing attention in recent years. The
deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have …
deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have …
Adapting fairness interventions to missing values
Missing values in real-world data pose a significant and unique challenge to algorithmic
fairness. Different demographic groups may be unequally affected by missing data, and the …
fairness. Different demographic groups may be unequally affected by missing data, and the …
Fairness and sequential decision making: Limits, lessons, and opportunities
As automated decision making and decision assistance systems become common in
everyday life, research on the prevention or mitigation of potential harms that arise from …
everyday life, research on the prevention or mitigation of potential harms that arise from …
Mitigating source bias for fairer weak supervision
Weak supervision enables efficient development of training sets by reducing the need for
ground truth labels. However, the techniques that make weak supervision attractive---such …
ground truth labels. However, the techniques that make weak supervision attractive---such …
Fairif: Boosting fairness in deep learning via influence functions with validation set sensitive attributes
Empirical loss minimization during machine learning training can inadvertently introduce
bias, stemming from discrimination and societal prejudices present in the data. To address …
bias, stemming from discrimination and societal prejudices present in the data. To address …
[PDF][PDF] Challenges for AI in healthcare systems
This paper overviews the challenges of using artificial intelligence (AI) methods when
building healthcare systems, as discussed at the AIsola Conference in 2023. It focuses on …
building healthcare systems, as discussed at the AIsola Conference in 2023. It focuses on …
From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms
When an individual reports a negative interaction with some system, how can their personal
experience be contextualized within broader patterns of system behavior? We study the …
experience be contextualized within broader patterns of system behavior? We study the …