  In the last five years, private companies, research institutions as well as
public sector organisations have issued principles and guidelines for ethical
AI, yet there is debate about both what constitutes "ethical AI" and which
ethical requirements, technical standards and best practices are needed for its
realization. To investigate whether a global agreement on these questions is
emerging, we mapped and analyzed the current corpus of principles and
guidelines on ethical AI. Our results reveal a global convergence emerging
around five ethical principles (transparency, justice and fairness,
non-maleficence, responsibility and privacy), with substantive divergence in
relation to how these principles are interpreted; why they are deemed
important; what issue, domain or actors they pertain to; and how they should be
implemented. Our findings highlight the importance of integrating
guideline-development efforts with substantive ethical analysis and adequate
implementation strategies.
