Ethical Implications of Using Deep Learning for Facial Recognition

Ethical Implications of Using Deep Learning for Facial Recognition | Zenodo Skip to main Communities My dashboard Log in Sign up Zenodo is currently experiencing slowness and intermittent outages due to heavy automated traffic from bots and AI crawlers. We are aware of the problem, and our team is focused on stabilizing the service. Thank you for your patience. Published April 25, 2026 | Version v1 Journal article Open Ethical Implications of Using Deep Learning for Facial Recognition Authors/Creators Dr. Gajanan Pandurang Khetri 1 Pradeep Janardhan Udhan 2 Show affiliations 1.

Tulsi Computer Science & I.T. College, Beed, State- Maharashtra, Country- India 2.

Senior Manager, (Soft. Develop. & Engg.) Charles Schwab, Texas, U.S.A. Description Facial recognition technology (FRT), powered by deep learning algorithms, has seen rapid adoption across various sectors, including law enforcement, security, and consumer electronics. While these systems offer significant benefits in terms of efficiency and accuracy, their deployment raises profound ethical concerns. This paper explores the ethical implications of using deep learning for facial recognition, focusing on issues such as privacy, bias, consent, and surveillance. Through an analysis of current research and case studies, this work highlights the need for robust ethical frameworks to govern the use of such technologies. The discussion is structured around key themes, including algorithmic fairness, data governance, and the societal impact of FRT. Files 071434.pdf Files (472.4 kB) Name Size Download all 071434.pdf md5:9be34366651c84eee2e694f307cd8f1d 472.4 kB Preview Download 34 Views 28 Downloads Show more details All versions This version Views Total views 34 34 Downloads Total downloads 28 28 Data volume Total data volume 13.7 MB 13.7 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Details DOI DOI Badge DOI 10.5281/zenodo.19756202 Markdown [](https://doi.org/10.5281/zenodo.19756202) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19756202.svg :target: https://doi.org/10.5281/zenodo.19756202 HTML <a href="https://doi.org/10.5281/zenodo.19756202"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19756202.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19756202.svg Target URL https://doi.org/10.5281/zenodo.19756202 Resource type Journal article Publisher Zenodo Published in International Journal of Advance and Applied Research, S7(14), 194 - 197, ISSN: 2347 -7075, 2026. Languages English Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Citation Export Technical metadata Created May 2, 2026 Modified May 2, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies

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