Ethics of AI: A Systematic Literature Review of Principles and Challenges
This work provides a foundational overview for policymakers and researchers addressing ethical issues in AI, though it is incremental as a review.
The authors conducted a systematic literature review to identify global consensus on AI ethics principles and challenges, finding 22 principles (e.g., transparency, privacy) and 15 challenges (e.g., lack of ethical knowledge).
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 assess the ethical capabilities of AI systems and provide best practices for further improvements.