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This cluster provides foundational knowledge and organizational orientation for applying AI responsibly across products and domains.
Covers conceptual foundations, terminology and orientation concepts for AI systems from a system-level perspective. Addresses architecture boundaries, model lifecycles, data integration points, interface definitions, evaluation metrics and interoperability requirements, as well as delimitations to security and governance concerns. Excludes detailed implementation guides, operational runbooks, cloud- or database-specific configuration details and detailed test strategies.
A concept for designing AI systems that places human needs, values and workflows at the center.
Socio-technical systems describe systems in which social and technical components are inseparably intertwined.