Ethics of AI: a systematic literature review of principles and challenges
Abstract
Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.
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The majority of shared concerns and challenges across professions are subsumed under the overarching category of ethical concerns.
“The literature review indicates that, although a range of concerns and challenges are shared across professions, the majority are subsumed under the overarching category of ethical concerns at this level.”
From From avoidance to adoption: strategies for integrating AI in higher education contexts (2026) · cites this paper as Khan et al., 2022
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Principles Governing Ethical Development and Deployment of AI
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Garcia Ramos, J. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1654805 · Cited by 8 · see citing works
From avoidance to adoption: strategies for integrating AI in higher education contexts
Gerard, J., Hadi, M. U., Natsheh, B. R. R., Singh, M., & Reid, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1869723
- Authors
- Khan, A. A., Badshah, S., Liang, P., Waseem, M., Khan, B., Ahmad, A., Fahmideh, M., Niazi, M., & Akbar, M. A.
- Affiliations
- University of Oulu (FI)
- Dalhousie University (CA)
- Wuhan University (CN)
- University of Loralai (PK)
- University of Ha'il (SA)
- University of Southern Queensland (AU)
- King Fahd University of Petroleum and Minerals (SA)
- Lappeenranta-Lahti University of Technology (FI)
- ORCID
- Venue
- The International Conference on Evaluation and Assessment in Software Engineering 2022
- Publisher
- ACM
- Published
- 2022-06-13
- Pages
- 383-392
- OpenAlex
- W3199110531
- Cited by
- 170
- References
- 12
- Citations by year
- 2026: 42 · 2025: 58 · 2024: 51 · 2023: 15 · 2022: 4
- FWCI
- 6.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Conference on Evaluation and Assessment in Software Engineering (EASE) (publishedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- 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
- AI ethics, ethical principles, AI ethics guidelines, transparency, privacy, accountability, fairness, ethical knowledge, barriers to adoption, maturity model, best practices
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