Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle.
These biases can have significant clinical consequences, especially in applications that …

Abstract Generative Artificial Intelligence (AI) models serve as powerful tools for
organizations aiming to integrate advanced data analysis and automation into their …

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

Software bias is an increasingly important operational concern for software engineers. We
present a large-scale, comprehensive empirical study of 17 representative bias mitigation …

There has been increasing concern within the machine learning community and beyond that
Artificial Intelligence (AI) faces a bias and discrimination crisis which needs AI fairness with …

Abstract Machine learning tasks may admit multiple competing models that achieve similar
performance yet produce conflicting outputs for individual samples---a phenomenon known …

Abstract Machine learning (ML) models can underperform on certain population groups due
to choices made during model development and bias inherent in the data. We categorize …

Existing private synthetic data generation algorithms are agnostic to downstream tasks.
However, end users may have specific requirements that the synthetic data must satisfy …

Fairness is a crucial non-functional requirement of modern software systems that rely on the
use of Artificial Intelligence (AI) to make decisions regarding our daily lives in application …