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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 …
Fairness testing: A comprehensive survey and analysis of trends
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …
concern among software engineers. To tackle this issue, extensive research has been …
Picking on the same person: Does algorithmic monoculture lead to outcome homogenization?
As the scope of machine learning broadens, we observe a recurring theme of algorithmic
monoculture: the same systems, or systems that share components (eg datasets, models) …
monoculture: the same systems, or systems that share components (eg datasets, models) …
[كتاب][B] Fairness and machine learning: Limitations and opportunities
S Barocas, M Hardt, A Narayanan - 2023 - books.google.com
An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …
fairness and machine learning. Fairness and Machine Learning introduces advanced …
Policy advice and best practices on bias and fairness in AI
The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …
Fairness and bias in algorithmic hiring: A multidisciplinary survey
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …
Algorithmic fairness is especially applicable in this domain due to its high stakes and …
Turning the tables: Biased, imbalanced, dynamic tabular datasets for ml evaluation
Evaluating new techniques on realistic datasets plays a crucial role in the development of
ML research and its broader adoption by practitioners. In recent years, there has been a …
ML research and its broader adoption by practitioners. In recent years, there has been a …
Fairness-aware machine learning engineering: how far are we?
Abstract Machine learning is part of the daily life of people and companies worldwide.
Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision …
Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision …
Bias on demand: a modelling framework that generates synthetic data with bias
Nowadays, Machine Learning (ML) systems are widely used in various businesses and are
increasingly being adopted to make decisions that can significantly impact people's lives …
increasingly being adopted to make decisions that can significantly impact people's lives …
An empirical analysis of racial categories in the algorithmic fairness literature
Recent work in algorithmic fairness has highlighted the challenge of defining racial
categories for the purposes of anti-discrimination. These challenges are not new but have …
categories for the purposes of anti-discrimination. These challenges are not new but have …