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
Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic AI
Parand A. Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith
Establishing and Evaluating Trustworthy AI: Overview and Research Challenges
Dominik Kowald, Sebastian Scher, Viktoria Pammer-Schindler, Peter Müllner, Kerstin Waxnegger, Lea Demelius, Angela Fessl, Maximilian Toller, Inti Gabriel Mendoza Estrada, Ilija Simic, Vedran Sabol, Andreas Truegler, Eduardo Veas, Roman Kern, Tomislav Nad, Simone Kopeinik