Ethical AI in Information Technology: Navigating Bias, Privacy, Transparency, and Accountability
Open accessAbstract
The rapid advancement of artificial intelligence (AI) technologies has fundamentally transformed the landscape of information technology (IT), offering unprecedented opportunities for innovation and efficiency. However, these advancements also bring significant ethical challenges, including issues of bias, privacy, transparency, and accountability. This paper explores these ethical challenges and proposes a comprehensive ethical framework for the responsible development and deployment of AI in IT. Through an examination of historical context, current trends, and detailed case studies, the framework aims to provide actionable guidelines to mitigate biases, protect privacy, enhance transparency, and ensure accountability in AI systems. By fostering ethical AI practices, this framework aspires to support the sustainable and equitable advancement of AI technologies, ultimately benefiting society as a whole
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AI tools have potential drawbacks, particularly risks of misinformation, privacy, transparency, and accountability.
“While AI tools have been identified as offering certain capabilities, considerable discussion has also addressed their potential drawbacks, particularly the risks of misinformation, privacy, transparency, and accountability (Sargiotis 2024; Overney 2025; Sarafis etal. 2025).”
From Digital civics in action: the promise and emerging practices of generative AI in grassroots community advocacy (2026) · cites this paper as Sargiotis 2024
Sections 4.2 (Privacy Concerns) and 4.3 (Transparency and Accountability) explicitly discuss those three risks of AI systems. The passages retrieved for “misinformation” and “fake news” do not address misinformation as a risk, so our copy does not support that part of the claim. Page numbers were not available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMaintaining transparency throughout data collection, usage, and sharing processes, along with applying data privacy safeguards, can contribute to mitigating user mistrust.
“Particularly in terms of privacy, maintaining transparency throughout data collection, usage, and sharing processes, along with applying data privacy safeguards, can contribute to mitigating user mistrust (Sargiotis 2024).”
From Digital civics in action: the promise and emerging practices of generative AI in grassroots community advocacy (2026) · cites this paper as Sargiotis 2024
Implementing a multi-actor approach (including AI experts) that actively engages citizens and integrates monitoring and iterative improvement processes can help mitigate potential risks.
“Additionally, implementing a multi-actor approach (including AI experts) that actively engages citizens and integrates monitoring and iterative improvement processes can help mitigate potential risks (Sargiotis 2024; Syukrina and Nugraha 2025).”
From Digital civics in action: the promise and emerging practices of generative AI in grassroots community advocacy (2026) · cites this paper as Sargiotis 2024
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Cites
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Üstündağ, B., Foth, M., & Seevinck, J. (2026) · AI & SOCIETY · doi:10.1007/s00146-026-03108-4
- Authors
- National Technical University of Athens, G., & Sargiotis, D.
- Affiliations
- National Technical University of Athens (GR)
- Corresponding author
- Sargiotis, Dimitrios
- ORCID
- Sargiotis, Dimitrios
- Venue
- Advances in Machine Learning & Artificial Intelligence
- Publisher
- Opast Group LLC
- Published
- 2024-09-09
- Volume
- 5
- Issue
- 3
- Pages
- 01-14
- ISSN
- 2769-545X (ISSN-L)
- OpenAlex
- W4402576628
- Cited by
- 34
- References
- 6
- Citations by year
- 2026: 17 · 2025: 17
- FWCI
- 14.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Advances in Machine Learning & Artificial Intelligence (publishedVersion, public-domain)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- ethical AI, AI ethics, algorithmic bias, privacy, transparency, accountability, ethical frameworks, responsible AI development, information technology
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