🔬 Research Summary by Josue Casco-Rodriguez and Sina Alemohammad. Josue is a 2nd-year PhD student at Rice University. He is interested in illuminating the intersection of machine learning and neuroscience from … [Read more...] about Self-Consuming Generative Models Go MAD
Articles
From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts
🔬 Research Summary by Vishakha Agrawal, an independent researcher interested in human-AI collaboration, participatory AI and AI safety. [Original paper by Vishakha Agrawal, Serhiy Kandul, Markus Kneer, and Markus … [Read more...] about From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
🔬 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