Artificial Intelligence System
Artificial intelligence (AI) systems are computational models designed to mimic human cognitive functions, with current research focusing on improving their reliability, safety, and explainability. Key areas of investigation include developing more robust and trustworthy AI models, often employing large language models (LLMs) and other deep learning architectures, as well as enhancing human-AI interaction and understanding AI decision-making processes through explainable AI (XAI) techniques. The field's impact spans diverse applications, from improving medical diagnoses and assisting in game design to enhancing cybersecurity and supporting policy analysis, while also raising crucial ethical and societal considerations.
Papers
Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems
Friederike Rohde, Josephin Wagner, Andreas Meyer, Philipp Reinhard, Marcus Voss, Ulrich Petschow, Anne Mollen
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities
Xudong Shen, Hannah Brown, Jiashu Tao, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez