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 …

Objectives Statistical and artificial intelligence algorithms are increasingly being developed
for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical …

To make models more understandable and correctable, I propose that the PROMISE
community pivots to the problem of model review. Over the years, there have been many …

Ensuring fairness in machine learning (ML) is vital, especially as these models are
increasingly used in socially critical financial decision-making processes such as credit …

Existing research mostly improves the fairness of Machine Learning (ML) software regarding
a single protected attribute at a time, but this is unrealistic given that many users have …

We present the first automated verification technique for confidence-based 2-safety
properties, such as global robustness and global fairness, in deep neural networks (DNNs) …

Machine learning (ML) is increasingly being used in critical decision-making software, but
incidents have raised questions about the fairness of ML predictions. To address this issue …

Because of the naturalness of software and the rapid evolution of Machine Learning (ML)
techniques, frequently repeated code change patterns (CPATs) occur often. They range from …

With the increasing utilization of Machine Learning (ML) software in critical domains such as
employee hiring, college admission, and credit evaluation, ensuring fairness in the decision …

Fairness is one of the socio-technical concerns that must be addressed in software systems.
Considering the popularity of mobile software applications (apps) among a wide range of …