通过明确评估上下文，让AI落地评估更贴近真实业务场景。
Making AI Evaluation Deployment Relevant Through Context Specification
- 将模糊的业务诉求转化为可测量的具体指标
- 帮助组织判断AI工具能否在实际环境中持续创造价值
- 适合关注AI落地效果的管理者和技术决策者
许多组织难以从AI部署中获得实际价值，对科学评估AI的需求日益迫切。当前的评估方法常掩盖实际运营中的关键因素，使决策者难以判断AI工具是否能带来持久效益。本文提出并阐述‘上下文规范’（context specification）这一过程，将不同利益相关方对特定场景下重要事项的模糊看法，转化为明确、可命名的评估维度：即对目标属性、行为与结果的清晰定义，使其可在具体环境中被观察与衡量。该过程为评估AI系统在组织真实管理情境下的表现提供了基础路线图。
原文摘要 · Abstract (English)
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.
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