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pro vyhledávání: '"Fresz, Benjamin"'
Autor:
Fresz, Benjamin, Göbels, Vincent Philipp, Omri, Safa, Brajovic, Danilo, Aichele, Andreas, Kutz, Janika, Neuhüttler, Jens, Huber, Marco F.
Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act. In this context, the black-box nature of machine learning models limits the use
Externí odkaz:
http://arxiv.org/abs/2408.02379
Publikováno v:
FAccT'24: The 2024 ACM Conference on Fairness, Accountability, and Transparency, June 2024, Pages 1-19
Decision processes of computer vision models - especially deep neural networks - are opaque in nature, meaning that these decisions cannot be understood by humans. Thus, over the last years, many methods to provide human-understandable explanations h
Externí odkaz:
http://arxiv.org/abs/2406.05068
This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI). While there is much discussion about the AI Act, for which the trilogue of the European Parliament, Council and Commission recently concluded, other a
Externí odkaz:
http://arxiv.org/abs/2404.12762
Autor:
Brajovic, Danilo, Renner, Niclas, Goebels, Vincent Philipp, Wagner, Philipp, Fresz, Benjamin, Biller, Martin, Klaeb, Mara, Kutz, Janika, Neuhuettler, Jens, Huber, Marco F.
Despite large progress in Explainable and Safe AI, practitioners suffer from a lack of regulation and standards for AI safety. In this work we merge recent regulation efforts by the European Union and first proposals for AI guidelines with recent tre
Externí odkaz:
http://arxiv.org/abs/2307.11525
Motion planning problems often have many local minima. Those minima are important to visualize to let a user guide, prevent or predict motions. Towards this goal, we develop the motion planning explorer, an algorithm to let users interactively explor
Externí odkaz:
http://arxiv.org/abs/1909.05035
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