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Acknowledgements

Published online by Cambridge University Press:  28 October 2022

Silja Voeneky
Affiliation:
Albert-Ludwigs-Universität Freiburg, Germany
Philipp Kellmeyer
Affiliation:
Medical Center, Albert-Ludwigs-Universität Freiburg, Germany
Oliver Mueller
Affiliation:
Albert-Ludwigs-Universität Freiburg, Germany
Wolfram Burgard
Affiliation:
Technische Universität Nürnberg

Summary

Type
Chapter
Information
The Cambridge Handbook of Responsible Artificial Intelligence
Interdisciplinary Perspectives
, pp. xix - xx
Publisher: Cambridge University Press
Print publication year: 2022
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC-ND 4.0 https://creativecommons.org/cclicenses/

Acknowledgements

We want to start by thanking the Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, for the immense support of our Saltus Responsible AI Research Group 2018–2021.Footnote * Without the FRIAS, its directors, especially Bernd Kortmann, and the team, this Handbook would not have been possible.

We would also like to thank the researchers at our Responsible AI Research Group and at the chair of Silja Voeneky, Alisa Pojtinger, Jonatan Klaedtke, and Daniel Feuerstack, who helped us edit this book, for their important work. Furthermore, we are very grateful to the research assistants Alena Huenermund (Responsible AI Research Group), Hannah Weber, and Heidi He (University of Cambridge) for their valuable support.

We would also like to acknowledge the Baden-Württemberg Stiftung for their financial support in the context of our research project AI Trust. Finally, we would like to express our thanks to the Klaus Tschira Foundation for supporting the work of editor Philipp Kellmeyer in the project “Neuroethics and AI Ethics Lab" (grant no. 00.001.2019).”

* UP 14/1 (2), DFG-ProjektID: 422010107.

Footnotes

* UP 14/1 (2), DFG-ProjektID: 422010107.

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