Making AI Evaluation Deployment Relevant Through Context Specification

TL;DR

This paper proposes context specification as a method to improve AI evaluation relevance by explicitly defining key properties and outcomes in deployment settings, aiding decision makers.

Contribution

It introduces a structured process for translating stakeholder perspectives into explicit evaluation constructs tailored to deployment contexts.

Findings

Enhances understanding of AI performance in real-world settings

Provides a roadmap for context-aware AI evaluation

Bridges the gap between evaluation metrics and operational realities

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 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.