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High-level Expert Group on AI

AI ethics-guideline not-research-ethics

“These guidelines are addressed to all AI stakeholders designing, developing, deploying, implementing, using or being affected by AI, including but not limited to companies, organisations, researchers, public services, government agencies, institutions, civil society organisations, individuals, workers and consumers. Stakeholders committed towards achieving Trustworthy AI can voluntarily opt to use these Guidelines as a method to operationalise their commitment, in particular by using the practical assessment list of Chapter III when developing, deploying or using AI systems. This assessment list can also complement – and hence be incorporated in – existing assessment processes. The Guidelines aim to provide guidance for AI applications in general, building a horizontal foundation to achieve Trustworthy AI. However, different situations raise different challenges. AI music recommendation systems do not” (High-Level Expert Group on AI, 2019, p. 5)“raise the same ethical concerns as AI systems proposing critical medical treatments. Likewise, different opportunities and challenges arise from AI systems used in the context of business-to-consumer, business-tobusiness, employer-to-employee and public-to-citizen relationships, or more generally, in different sectors or use cases. Given the context-specificity of AI systems, the implementation of these Guidelines needs to be adapted to the particular AI-application. Moreover, the necessity of an additional sectorial approach, to complement the more general horizontal framework proposed in this document, should be explored.” (High-Level Expert Group on AI, 2019, p. 6)

Rights
Strategies Consider tensions and absolute rights Overview of considerations in transparency Considerations in assessing trustworthy AI - Fundamental rights Considerations in assessing trustworthy AI - human agency Considerations in assessing trustworthy AI - Resilience to attack and security Considerations in assessing trustworthy AI - Human oversight Considerations in assessing trustworthy AI - Fallback plan and general safety Considerations in assessing trustworthy AI - Accuracy Considerations in assessing trustworthy AI - Reliability and reproducibility Considerations in assessing trustworthy AI - Respect for privacy and data protection Considerations in assessing trustworthy AI - Quality and integrity of data Considerations in assessing trustworthy AI - Access to data Considerations in assessing trustworthy AI - Traceability Considerations in assessing trustworthy AI - Communication Considerations in assessing trustworthy AI - Unfair bias avoidance Considerations in assessing trustworthy AI - Accessibility and universal design Considerations in assessing trustworthy AI - Stakeholder participation Considerations in assessing trustworthy AI - Sustainable and environmentally friendly AI Considerations in assessing trustworthy AI - Social impact Considerations in assessing trustworthy AI - Society and democracy Considerations in assessing trustworthy AI - Auditability Considerations in assessing trustworthy AI - Minimising and reporting negative impacts Considerations in assessing trustworthy AI - Documenting trade-offs Questions to consider in key stages of AI and machine learning based research, regarding transparency and explainability
Title High-level Expert Group on AI