The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Balancing ChatGPT and Data Protection in Germany: Challenges and Opportunities for Policy Makers
Abstract
In the last few months there has been widespread discussion about the remarkable progress made in the field of artificial intelligence, specifically large language models such as "ChatGPT". The ethical implications of AI, particularly concerning data protection, have sparked discussions on the necessity of robust regulations. This article examines the intersection of data protection, ChatGPT, and the ethics of AI, it explores Germany's ongoing efforts to strike a balance between harnessing the potential of large language models as ChatGPT and ensuring responsible and transparent use of AI technology in the policy-making realm. The GDPR serves as a guiding framework, necessitating careful consideration of privacy rights and secure handling of personal data when deploying ChatGPT in Germany's policy-making processes. The study draws on analysis on the current laws and regulations of data protection in Germany while studying Germany's commitment to safeguarding personal information through the active presence of The German Federal Commissioner for Data Protection and Freedom of Information. The first section provides a context and presents the policy problem. The second section looks at the available policy options on the role of policymaking in establishing comprehensive regulations regarding the use of ChatGPT and generative AI. The third section provides recommendations on how Germany can ensure the responsible management of ChatGPT, through strengthening data protection laws and regulations, simultaneously, restricting ChatGPT usage to private users and government, also, embracing appropriate usage of generative AI while developing ethical guidelines and best practices to harness its benefits, fostering innovation and advancement.
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“Generative AI models may violate privacy and data protection laws due to pervasive training on (partially) personal data, the memorization of training data, inversion attacks [20], interactions with users (e.g., prompts containing personal data of third parties) [2, Technical Report, p. 2] [89], and the output the AI produces.”
From Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity (2024) · cites this paper as [89]
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Citations
Cites
Principles alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886
Principles of Information Security
Whitman, M. E., & Mattord, H. J. (2002) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 993
Privacy in Context: Technology, Policy, and the Integrity of Social Life
Han, S. (2011) · Journal of Information Policy · doi:10.5325/jinfopoli.1.2011.0149 · Cited by 462
ChatGPT: Evolution or revolution?
Gordijn, B., & Have, H. T. (2023) · Medicine, Health Care and Philosophy · doi:10.1007/s11019-023-10136-0 · Cited by 317
Privacy in context technology, policy, and the integrity of social life
Nissenbaum, H. 1. (2009) · Cited by 317
(2019) · International series on intelligent systems, control and automation: science and engineering · doi:10.1007/978-3-030-12524-0 · Cited by 32
Nalbandian, L. (2022) · Journal of Responsible Technology · doi:10.1016/j.jrt.2021.100023 · Cited by 21
The Conceptual Basis of Personal Information in Australian Privacy Law
Burdon, M., & Telford, P. (2010) · QUT ePrints (Queensland University of Technology) · Cited by 17
Privacy Law Issues in Blockchains: An Analysis of PIPEDA, the GDPR, and Proposals for CompliancePreprint
Walters, N. (2019) · SSRN Electronic Journal · Cited by 4
Data Protection by Definition – Report on the Law of Data Disclosure in JapanPreprint
Hoffmann, T. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4055510 · Cited by 3
Cited by
Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity
Novelli, C., Casolari, F., Hacker, P., Spedicato, G., & Floridi, L. (2024) · Computer Law & Security Review · doi:10.1016/j.clsr.2024.106066 · Cited by 124
History, development, and principles of large language models: an introductory survey
Wang, Z., Chu, Z., Doan, T. V., Ni, S., Yang, M., & Zhang, W. (2025) · AI and Ethics · doi:10.1007/s43681-024-00583-7 · Cited by 109
“This increases the risk of violating privacy regulations, such as the General Data Protection Regulation [148].”
Metadata
- Authors
- Leboukh, F., Baba Aduku, E., & Ali, O.
- Venue
- Journal of Politics and Ethics in New Technologies and AI
- Publisher
- National Documentation Centre (EKT)
- Published
- 2023-08-04
- Volume
- 2
- Issue
- 1
- Pages
- e35166
- ISSN
- 2944-9243
- Cited by
- 18
- References
- 10
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- Leboukh, F., Baba Aduku, E., & Ali, O. (2023). Balancing ChatGPT and Data Protection in Germany: Challenges and Opportunities for Policy Makers. Journal of Politics and Ethics in New Technologies and AI. https://doi.org/10.12681/jpentai.35166
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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