Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions

no code implementations • 30 Oct 2023 • Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Prismal view of ethics

no code implementations • 26 May 2022 • Sarah Isufi, Kristijan Poje, Igor Vukobratovic, Mario Brcic

We shall have a hard look at ethics and try to extract insights in the form of abstract properties that might become tools.

Ethics

Explainability in reinforcement learning: perspective and position

no code implementations • 22 Mar 2022 • Agneza Krajna, Mario Brcic, Tomislav Lipic, Juraj Doncevic

Most articles dealing with explainability in artificial intelligence provide methods that concern supervised learning and there are very few articles dealing with this in the area of RL.

Position Recommendation Systems +3

Impossibility Results in AI: A Survey

no code implementations • 1 Sep 2021 • Mario Brcic, Roman V. Yampolskiy

Also, we added new results (theorems) such as the unfairness of explainability, the first explainability-related result in the induction category.

Decision Making Ethics

AI safety: state of the field through quantitative lens

no code implementations • 12 Feb 2020 • Mislav Juric, Agneza Sandic, Mario Brcic

While there exist survey papers for the field of AI safety, there is a lack of a quantitative look at the research being conducted.