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 …

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 …

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 …

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

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 …

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 …

At present, supervised machine learning algorithms are ubiquitously used to learn predictive
models that have a major impact on people's lives. However, the vast majority of such …

Machine learning is becoming ubiquitous. From finance to medicine, machine learning
models are boosting decision-making processes and even outperforming humans in some …