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

Khan, Arif Ali; Badshah, Sher; Liang, Peng; Waseem, Muhammad; Khan, Bilal; Ahmad, Aakash; Fahmideh, Mahdi; Niazi, Mahmood; Akbar, Muhammad Azeem
 
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https://doi.org/10.1145/3530019.3531329

Khan, Arif Ali
Badshah, Sher
Liang, Peng
Waseem, Muhammad
Khan, Bilal
Ahmad, Aakash
Fahmideh, Mahdi
Niazi, Mahmood
Akbar, Muhammad Azeem
Association for Computing Machinery

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. https://doi.org/10.1145/3530019.3531329

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© 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), http://dx.doi.org/10.1145/3530019.3531329.
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doi:https://doi.org/10.1145/3530019.3531329
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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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