As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …

Data valuation, a growing field that aims at quantifying the usefulness of individual data
sources for training machine learning (ML) models, faces notable yet often overlooked …

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …

Collaborative machine learning involves training models on data from multiple parties but
must incentivize their participation. Existing data valuation methods fairly value and reward …

Explainable AI (XAI) refers to the development of AI systems and machine learning models
in a way that humans can understand, interpret and trust the predictions, decisions and …

Over the last few years, Shapley value (SV), a solution concept from cooperative game
theory, has found numerous applications in data analytics (DA). This paper provides the first …

In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data.
However, traditional data valuation methods are not suitable due to privacy concerns. To …