Making AI Evaluation Deployment Relevant Through Context Specification
For organizational decision makers struggling with AI deployment, this provides a structured approach to make evaluations more relevant, though it is a conceptual framework without empirical validation.
The paper introduces context specification as a process to help organizations evaluate AI systems by defining relevant properties, behaviors, and outcomes for deployment contexts, addressing the gap between standard evaluation and operational realities.
With many organizations struggling to gain value from AI deployments, pressure to evaluate AI in an informed manner has intensified. Status quo AI evaluation approaches often mask the operational realities that ultimately determine deployment success, making it difficult for organizational decision makers to know whether and how AI tools will deliver durable value. We introduce and describe context specification as a process to support and inform this decision making process. Context specification turns diffuse stakeholder perspectives about what matters in a given setting into clear, named constructs: explicit definitions of the properties, behaviors, and outcomes that evaluations aim to capture, so they can be observed and measured in context. The process serves as a foundational roadmap for evaluating what AI systems are likely to do in the deployment contexts that organizations actually manage.