AI Ethics Issues in Real World: Evidence from AI Incident Database

Existing AI ethics guidelines are too vague to be effective: Despite governments and corporations creating multiple AI ethics guidelines, their real-world impact has been limited, suggesting that abstract principles need more concrete, practical guidance for implementation.

AI ethics problems cluster in specific application areas: The research identified 13 high-risk application domains, with intelligent service robots, language/vision models, and autonomous driving systems experiencing the most documented ethical incidents—indicating where oversight and safeguards should be prioritized.

AI ethics issues take eight distinct forms: Rather than treating "AI ethics" as a monolithic concern, the paper identifies eight specific problem categories including inappropriate use, racial discrimination, physical safety risks, and unfair algorithms, each requiring different solutions and preventive measures.

Real-world incident data reveals practical patterns: By analyzing actual documented AI failures in the AI Incident Database rather than theoretical scenarios, the research provides evidence-based insights into how ethical problems actually manifest, helping practitioners anticipate and prevent similar failures.

Practitioners need actionable frameworks, not just principles: The paper's taxonomy of ethics issues and application areas is designed to give AI developers and deployers concrete, practical guidance for ethical deployment—moving beyond high-level guidelines to specific problem categories they should monitor for in their work.