AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers

Transparency, accountability, and privacy are the top priorities: The study of 99 AI practitioners and lawmakers across 20 countries identified these three principles as most critical for ethical AI, suggesting that stakeholders prioritize being able to understand AI decisions, assign responsibility for outcomes, and protect personal data.

Knowledge gaps and regulatory voids are major obstacles: The research found that "lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies" are the most common challenges preventing AI ethics implementation, indicating that the field needs both better education and stronger institutional structures to enforce ethical standards.

Practitioners and lawmakers have different perspectives on key issues: The study found "statistically correlated with significant differences" between these two groups on principles like fairness and freedom, as well as challenges like monitoring and "machine distortion," suggesting that building consensus on AI ethics requires bridging these different viewpoints.

Conflicts between principles pose a severe practical problem: The analysis revealed that "conflict in practice is a highly severe challenge," meaning that different ethical principles sometimes contradict each other in real-world AI applications—a tension that needs to be addressed in implementation strategies.

Global consensus is emerging but incomplete: Drawing from 99 representatives across five continents, the study suggests there is international agreement on core AI ethics principles, though significant gaps remain in how to enforce them and resolve disagreements between stakeholders.