🔬 Research Summary by Faisal Hamman, a Ph.D. student at the University of Maryland, College Park. Faisal’s research focuses on Fairness, Explainability, and Privacy in Machine Learning, where he brings novel foundational … [Read more...] about Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
Acceptable Risks in Europe’s Proposed AI Act: Reasonableness and Other Principles for Deciding How Much Risk Management Is Enough
🔬 Research Summary by Dr. Henry Fraser, a Research Fellow in Law, Accountability, and Data Science at the Centre of Excellence for Automated Decision-Making and Society. [Original paper by Henry Fraser and … [Read more...] about Acceptable Risks in Europe’s Proposed AI Act: Reasonableness and Other Principles for Deciding How Much Risk Management Is Enough
Open-source provisions for large models in the AI Act
🔬 Research Summary by Harry Law and Sebastien A. Krier. Harry Law is an ethics and policy researcher at Google DeepMind, a PhD candidate at the University of Cambridge, and postgraduate fellow at the Leverhulme … [Read more...] about Open-source provisions for large models in the AI Act
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
🔬 Research Summary by Matthew Barker, a recent graduate from the University of Cambridge, whose research focuses on explainable AI and human-machine teams. [Original paper by Matthew Barker, Emma Kallina, … [Read more...] about FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
The path toward equal performance in medical machine learning
🔬 Research Summary by Eike Petersen, a postdoctoral researcher at the Technical University of Denmark (DTU), working on fair, responsible, and robust machine learning for medicine. [Original paper by Eike … [Read more...] about The path toward equal performance in medical machine learning