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This cluster provides foundational knowledge and organizational orientation for applying AI responsibly across products and domains.
Concept for deliberately extending human capabilities through tools, processes and interfaces to support decision-making and productivity.
Automation refers to automating repetitive tasks and processes using software, scripts or tools to reduce manual work and human error.
Practical approach to applying AI methods to real business and technical problems, focusing on operationalization, data integration and measurable outcomes.
A high-level concept for building systems that perform tasks requiring human-like perception, learning and decision-making.
Explains systematic distortions in data and models, their causes, and practical measures to detect and mitigate bias in AI systems.
A conceptual framework describing knowledge gaps, variability and ambiguity that influence decisions in engineering, architecture and product management.
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.