Making AI Evaluation Deployment Relevant Through Context Specification
Current AI evaluation methods often operate in abstraction from real-world deployment contexts, failing to assess an AI system’s capacity to sustainably generate value within specific organizations. This work proposes a novel “contextual specification” framework that leverages qualitative modeling and collaborative stakeholder analysis to transform ambiguous, context-dependent elements into clearly defined, nameable constructs. By explicitly delineating the attributes, behaviors, and outcomes that warrant evaluation, the framework establishes a set of observable and measurable context-sensitive metrics. This approach provides organizations with an actionable evaluation roadmap, effectively bridging the gap between technical performance and business value, thereby substantially enhancing the relevance and efficacy of AI deployment decisions.