Machine Learning (ML) software can lead to unfair and unethical decisions, making software
fairness bugs an increasingly significant concern for software engineers. However …

Machine Learning (ML) software has been widely adopted in modern society, with reported
fairness implications for minority groups based on race, sex, age, etc. Many recent works …

In the literature of mitigating unfairness in machine learning (ML), many fairness measures
are designed to evaluate predictions of learning models and also utilized to guide the …

One of the main challenges of machine learning is to ensure that its applications do not
generate or propagate unfair discrimination based on sensitive characteristics such as …

Bias mitigators can improve algorithmic fairness in machine learning models, but their effect
on fairness is often not stable across data splits. A popular approach to train more stable …

Abstract As reliance on Machine Learning (ML) systems in real-world decision-making
processes grows, ensuring these systems are free of bias against sensitive demographic …

Fairness has become an essential problem in many domains of Machine Learning (ML),
such as classification, natural language processing, and Generative Adversarial Networks …

As artificial intelligence (AI) models continue to scale up, they are becoming more capable
and integrated into various forms of decision-making systems. For models involved in moral …

Machine Learning (ML) models trained on biased data can reproduce and even amplify
these biases. Since such models are deployed to make decisions that can affect people's …

There are several bias mitigators that can reduce algorithmic bias in machine learning
models but, unfortunately, the effect of mitigators on fairness is often not stable when …