Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
Perceived diversity in software engineering: a systematic literature review
We define perceived diversity as the diversity factors that individuals are born with.
Perceived diversity in Software Engineering has been recognized as a high-value team …
Perceived diversity in Software Engineering has been recognized as a high-value team …
A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline
In recent years, many incidents have been reported where machine learning models
exhibited discrimination among people based on race, sex, age, etc. Research has been …
exhibited discrimination among people based on race, sex, age, etc. Research has been …
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 …
MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software
Machine Learning (ML) software can lead to unfair and unethical decisions, making software
fairness bugs an increasingly significant concern for software engineers. However …
fairness bugs an increasingly significant concern for software engineers. However …
A comprehensive empirical study of bias mitigation methods for machine learning classifiers
Software bias is an increasingly important operational concern for software engineers. We
present a large-scale, comprehensive empirical study of 17 representative bias mitigation …
present a large-scale, comprehensive empirical study of 17 representative bias mitigation …
Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and …
The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
Fairway: a way to build fair ML software
Machine learning software is increasingly being used to make decisions that affect people's
lives. But sometimes, the core part of this software (the learned model), behaves in a biased …
lives. But sometimes, the core part of this software (the learned model), behaves in a biased …
Are my deep learning systems fair? An empirical study of fixed-seed training
Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …