Journal article icon

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

Actions

Access Document

Files:
Publisher copy:
10.1080/08839514.2025.2463722

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0001-5629-6857


More from this funder
Funder identifier:
https://ror.org/0439y7842
Grant:
EP/S035362/1
More from this funder
Funder identifier:
https://ror.org/001aqnf71


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


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP