走向负责的AI：表征系统故障的人机混合分析
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
摘要：
随着机器学习系统从计算机科学实验室进入开放世界，它们的问责制成为一个高度优先的问题。问责要求对系统行为及其失败有深刻的理解。目前的评估方法，如单分数错误度量和混淆矩阵提供了隐藏重要缺陷的系统性能的汇总视图。了解有关故障的详细信息对于确定改进路径、传达不同环境中系统的可靠性以及指定适当的人为监督和参与非常重要。故障和缺点的表征对于由多个机器学习组件组成的系统特别复杂。对于这样的系统，现有的评价方法在描述和解释输入内容、系统组件的内部状态和最终输出质量之间的关系方面表现力有限。我们提出潘多拉，一套混合人机方法和工具，用于描述和解释系统故障。Pandora利用人类和系统生成的观察结果来总结系统在输入内容和系统架构方面出现故障的情况。我们分享了一个案例研究的结果，其中包含用于图像描述的机器学习管道，展示了详细的性能视图如何有利于分析和调试。
英文摘要：
As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Current evaluation methods such as single-score error metrics and confusion matrices provide aggregate views of system performance that hide important shortcomings. Understanding details about failures is important for identifying pathways for refinement, communicating the reliability of systems in different settings, and for specifying appropriate human oversight and engagement. Characterization of failures and shortcomings is particularly complex for systems composed of multiple machine learned components. For such systems, existing evaluation methods have limited expressiveness in describing and explaining the relationship among input content, the internal states of system components, and final output quality. We present Pandora, a set of hybrid human-machine methods and tools for describing and explaining system failures. Pandora leverages both human and system-generated observations to summarize conditions of system malfunction with respect to the input content and system architecture. We share results of a case study with a machine learning pipeline for image captioning that show how detailed performance views can be beneficial for analysis and debugging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
