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

Summary

This groundbreaking 2022 research paper fills a critical gap in AI ethics by providing the first comprehensive empirical study comparing how AI practitioners and lawmakers actually perceive ethical challenges in practice. Rather than proposing yet another theoretical framework, researchers surveyed 99 professionals across 20 countries on five continents to uncover real-world perspectives on AI ethics implementation, cultural differences in ethical priorities, and the disconnect between policy intentions and technical realities. The study reveals surprising consensus on some issues while highlighting significant cultural and professional divides on others.

What makes this research groundbreaking

• First of its kind methodology: Unlike theoretical AI ethics papers, this study uses rigorous empirical methods to capture actual stakeholder views rather than academic speculation about what practitioners think.
• Global scope with cultural nuance: The 20-country sample reveals how cultural contexts shape AI ethics priorities - for instance, privacy concerns vary dramatically between European and Asian respondents, while algorithmic fairness definitions differ across legal traditions.
• Practitioner-policymaker gap analysis: The research quantifies the disconnect between what lawmakers prioritize in AI regulation and what practitioners see as the most pressing ethical challenges in their daily work.
• Cross-continental representation: With participants from North America, Europe, Asia, Africa, and Australia, the study avoids the Western-centric bias common in AI ethics literature.

Key findings that challenge conventional wisdom

• Consensus isn't where you'd expect: While debates rage about algorithmic bias, practitioners and lawmakers surprisingly agreed on core principles but disagreed sharply on implementation priorities and timelines.
• Cultural factors override professional identity: Geographic location predicted ethical priorities more strongly than whether someone was a practitioner or policymaker, suggesting AI governance needs region-specific approaches.
• Implementation barriers are human, not technical: The biggest obstacles to ethical AI aren't computational challenges but organizational culture, resource allocation, and unclear accountability structures.
• Regulatory timing preferences diverge: Practitioners favored gradual, iterative governance development while lawmakers preferred comprehensive upfront frameworks - a tension that explains many current policy struggles.

Who this resource is for

• Policy researchers and government advisors developing evidence-based AI regulation who need empirical data on stakeholder perspectives rather than theoretical recommendations.
• AI governance professionals designing ethics programs who want to understand how their challenges compare globally and what implementation approaches resonate across cultures.
• Academic researchers in AI ethics, science and technology studies, or comparative policy analysis looking for robust empirical baselines for their theoretical work.
• International organizations (UN, OECD, ISO) developing global AI standards who need insights into cross-cultural variations in ethical priorities and governance preferences.
• Corporate AI ethics teams seeking to benchmark their challenges against global peers and understand how regional differences might affect their governance strategies.

Research limitations to consider

• Sample size constraints: While globally diverse, 99 respondents limits statistical power for some cross-cultural comparisons, particularly for underrepresented regions.
• Timing considerations: Data collected in 2021-2022 predates major developments like ChatGPT's release and the EU AI Act's finalization, potentially dating some findings.
• Definition variations: "AI practitioner" encompasses roles from ML engineers to product managers, while "lawmakers" includes both elected officials and regulatory staff - internal variation within groups may be significant.
• Self-selection bias: Participants volunteered for an AI ethics study, suggesting they may be more ethics-conscious than typical practitioners or policymakers in their regions.