Journal article : Review
Ai ethics: integrating transparency, fairness, and privacy in AI development
- Abstract:
- The expansion of Artificial Intelligence in sectors such as healthcare, finance, and communication has raised critical ethical concerns surrounding transparency, fairness, and privacy. Addressing these issues is essential for the responsible development and deployment of AI systems. This research establishes a comprehensive ethical framework that mitigates biases and promotes accountability in AI technologies. A comparative analysis of international AI policy frameworks from regions including the European Union, United States, and China is conducted using analytical tools such as Venn diagrams and Cartesian graphs. These tools allow for a visual and systematic evaluation of the ethical principles guiding AI development across different jurisdictions. The results reveal significant variations in how global regions prioritize transparency, fairness, and privacy, with challenges in creating a unified ethical standard. To address these challenges, we propose technical strategies, including fairness-aware algorithms, routine audits, and the establishment of diverse development teams to ensure ethical AI practices. This paper provides actionable recommendations for integrating ethical oversight into the AI lifecycle, advocating for the creation of AI systems that are both technically sophisticated and aligned with societal values. The findings underscore the necessity of global collaboration in fostering ethical AI development.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 5.1MB, Terms of use)
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- Publisher copy:
- 10.1080/08839514.2025.2463722
Authors
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/S035362/1
- Publisher:
- Taylor & Francis
- Journal:
- Applied Artificial Intelligence More from this journal
- Volume:
- 39
- Issue:
- 1
- Article number:
- e2463722
- Publication date:
- 2025-02-07
- Acceptance date:
- 2025-02-02
- DOI:
- EISSN:
-
1087-6545
- ISSN:
-
0883-9514
- Language:
-
English
- Subtype:
-
Review
- Pubs id:
-
2085532
- Local pid:
-
pubs:2085532
- Deposit date:
-
2025-05-24
- ARK identifier:
Terms of use
- Copyright holder:
- Petar Radanliev
- Copyright date:
- 2025
- Rights statement:
- © 2025 The Author(s). Published with license by Taylor & Francis Group, LLC.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s)or with their consent.
- Licence:
- CC Attribution (CC BY)
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