Making AI Evaluation Deployment Relevant Through Context Specification

TL;DR

Context specification is introduced and described as a process to support and inform the deployment decision making process and serves as a foundational roadmap for evaluating what AI systems are likely to do in the deployment contexts that organizations actually manage.

Abstract

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 mask the operational realities that ultimately determine deployment success, making it difficult for decision makers outside the stack to know whether and how AI tools will deliver durable value. We introduce and describe context specification as a process to support and inform the deployment 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.

Figures (2)

• Figure 1: Context specification serves as the "Contextualize" step in the CIRCLE real-world AI evaluation lifecycle fromrealitycheck.
• Figure 2: Context specification as the deployment-to-evaluation translation step: turning stakeholder priority items into evaluable constructs and evidence needs.