Research Article

This paper introduces LearnerAgent, a simulation framework built on large language models to investigate how learning behaviours evolve over time.

By John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein Published: May 2024 | arXiv:2301.10226v4 Overview...

By Google Research and DeepMind Published: July 2025 |   arXiv:2506.15692 Overview This research presents MLE-Star, a modular...

As AI reshapes sectors from banking to healthcare, lofty ethical principles—fairness, transparency, privacy—remain insufficient on their own. This research paper dives beneath the surface to ask: How do we operationalize these ideals? The authors pinpoint a crucial gap: ethical frameworks often lack enforceable practices. They explore real-world obstacles—data bias, opaque models, missing regulations, siloed teams—and chart a proactive route forward: define measurable requir

Ethical AI isn’t just about doing what’s right—it’s about showing it, every step of the way. This Patterns paper breaks down how to apply principles like transparency, fairness, and accountability into measurable AI system requirements. It offers a clear, action‑oriented framework for building and evaluating trustworthy AI—from the data pipeline to deployment and beyond.

AI is transforming every aspect of our lives—but it also raises pressing questions about fairness, privacy, and accountability. This Patterns (Cell Press) paper by Kazim & Koshiyama offers a thoughtful, cross‑disciplinary introduction to AI ethics, laying out key principles and diagnosing where current frameworks fall short. At its heart, the paper emphasizes three pillars —accountability, responsibility, and transparency— and highlights the urgent need to bring these from t