An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

Much of the previous machine learning (ML) fairness literature assumes that protected
features such as race and sex are present in the dataset, and relies upon them to mitigate …

The recent advance of the federated learning (FL) has brought new opportunities for privacy-
aware distributed machine learning (ML) applications to train a powerful ML model without …

Detecting, measuring and mitigating various measures of unfairness are core aims of
algorithmic fairness research. However, the most prominent approaches require access to …

Existing regulations often prohibit model developers from accessing protected attributes
(gender, race, etc.) during training. This leads to scenarios where fairness assessments …

Graph neural networks (GNNs) have gained significant attraction due to their expansive real-
world applications. To build trustworthy GNNs, two aspects-fairness and privacy-have …

Existing algorithms for ensuring fairness in AI use a single-shot training strategy, where an
AI model is trained on an annotated training dataset with sensitive attributes and then fielded …

Providing trust in machine learning (ML) systems and their fairness is a socio-technical
challenge, and while the use of ML continues to rise, there is lack of adequate processes …

During the public trials of ChatGPT, it was highlighted that the language model can generate
racially discriminatory responses. This issue, however is not new to AI. Several models and …

The advent of 6G networks is anticipated to introduce a myriad of new technology enablers,
including heterogeneous radio, RAN softwarization, multi-vendor deployments, and AI …