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pro vyhledávání: '"Oelke, Daniela"'
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global level explain model behavior and on a local level explain sample predic
Externí odkaz:
http://arxiv.org/abs/2307.08494
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual
Externí odkaz:
http://arxiv.org/abs/2012.04344
Publikováno v:
2019 ICCV Workshop on Interpreting and Explaining Visual Artificial Intelligence Models
Explainable Artificial Intelligence (XAI) methods are typically deployed to explain and debug black-box machine learning models. However, most proposed XAI methods are black-boxes themselves and designed for images. Thus, they rely on visual interpre
Externí odkaz:
http://arxiv.org/abs/1909.07082
Autor:
Gu, Jindong, Oelke, Daniela
Publikováno v:
1st Workshop on Visualization for AI Explainability in 2018 IEEE Vis
Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or ev
Externí odkaz:
http://arxiv.org/abs/1909.01866
The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d81b3a935b8e6c79a628fd10aa1b92b
The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______715::e7b0996740b36cd59e80f2d09c894a5b
Akademický článek
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Artificial intelligence (AI), and in particular machine learning algorithms, are of increasing importance in many application areas but interpretability and understandability as well as responsibility, accountability, and fairness of the algorithms'
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::835c8a79e21753ca09fa49b90e028567
This report documents the program and the outcomes of Dagstuhl Seminar 19452 "Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable".
Dagstuhl Reports, Volume 9, Issue 11, pages 24-33
Dagstuhl Reports, Volume 9, Issue 11, pages 24-33
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d08259f620be1c2fd3fabf7b440dbad0
Publikováno v:
Computer Graphics Forum. Jun2011, Vol. 30 Issue 3, p871-880. 10p. 2 Illustrations.
