University of Oulu

Arif Ali Khan, Sher Badshah, Peng Liang, Muhammad Waseem, Bilal Khan, Aakash Ahmad, Mahdi Fahmideh, Mahmood Niazi, and Muhammad Azeem Akbar. 2022. Ethics of AI: A Systematic Literature Review of Principles and Challenges. In Proceedings of the International Conference on Evaluation and Assessment in Software Engineering 2022 (EASE '22). Association for Computing Machinery, New York, NY, USA, 383–392.

Ethics of AI : a systematic literature review of principles and challenges

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Author: Khan, Arif Ali1; Badshah, Sher2; Liang, Peng3;
Organizations: 1M3S Empirical Software Engineering Research Unit, University of Oulu, Finland
2Faculty of Computer Science, Dalhousie University, Canada
3School of Computer Science, Wuhan University, China
4Department of Computer Science, University Of Loralai, Pakistan
5College of Computer Science and Engineering, University of Hail, Saudi Arabia
6School of Business, University of Southern Queensland, Australia
7Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Finland
8Software Engineering Department, Lappeenranta-Lahti University of Technology, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
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Language: English
Published: Association for Computing Machinery, 2022
Publish Date: 2023-03-31


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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ISBN Print: 978-1-4503-9613-4
Pages: 383 - 392
DOI: 10.1145/3530019.3531329
Host publication: Proceedings of The ACM International Conference on Evaluation and Assessment in Software Engineering (EASE) 2022, June 13-15, 2022, Gothenburg, Sweden
Host publication editor: Staron, Miroslaw
Berger, Christian
Simmonds, Jocelyn
Prikladnicki, Rafael
Conference: The International Conference on Evaluation and Assessment in Software Engineering
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Copyright information: © ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the International Conference on Evaluation and Assessment in Software Engineering 2022 (EASE '22),