论文标题
测量AI系统超出准确性
Measuring AI Systems Beyond Accuracy
论文作者
论文摘要
当前的测试和评估（T＆E）评估机器学习（ML）系统性能通常取决于不完整的指标。测试通常是从ML系统生命周期的其他阶段孤立的。需要研究ML T＆E的跨域方法，以推动最先进的状态并建立人工智能（AI）工程学科。本文提倡通过概述指导整体T＆E策略的六个关键问题来采用强大的集成方法进行测试。
Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics. Testing is additionally often siloed from the other phases of the ML system lifecycle. Research investigating cross-domain approaches to ML T&E is needed to drive the state of the art forward and to build an Artificial Intelligence (AI) engineering discipline. This paper advocates for a robust, integrated approach to testing by outlining six key questions for guiding a holistic T&E strategy.
