  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.
