General Principle
Research on general principles in artificial intelligence focuses on establishing foundational rules and methodologies for developing reliable, ethical, and efficient AI systems. Current efforts concentrate on aligning AI behavior with human values, improving the generalization capabilities of models (like LLMs and deep learning architectures), and developing robust and interpretable methods for training and evaluating AI agents. This work is crucial for advancing AI safety, promoting responsible AI development, and enabling the broader adoption of AI across various scientific and practical applications.
Papers
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
Tobias Hyrup, Anton Danholt Lautrup, Arthur Zimek, Peter Schneider-Kamp
NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs
Maria Antoniak, Aakanksha Naik, Carla S. Alvarado, Lucy Lu Wang, Irene Y. Chen
Computational Approaches for Traditional Chinese Painting: From the "Six Principles of Painting" Perspective
Wei Zhang, Jian-Wei Zhang, Kam Kwai Wong, Yifang Wang, Yingchaojie Feng, Luwei Wang, Wei Chen
Acceptable risks in Europe's proposed AI Act: Reasonableness and other principles for deciding how much risk management is enough
Henry Fraser, Jose-Miguel Bello y Villarino