AI数据开发：面向系统卡框架的评分卡
AI Data Development: A Scorecard for the System Card Framework
AI总结：
本文提出一种基于系统卡框架的AI数据集开发评分卡，从数据字典、收集、组成、动机和预处理五个维度评估数据质量，并在四个数据集上验证其能提供定制化改进建议，以提升AI系统的透明度与公平性。
摘要：
人工智能已深刻变革从医疗到金融的众多行业，通过自动化系统增强决策能力。然而，这些系统的可靠性主要取决于底层数据集的质量，这引发了人们对透明度、问责制和潜在偏见的持续担忧。本文提出了一种评分卡，用于评估AI数据集的开发，重点关注系统卡框架数据开发生命周期中的五个关键领域：数据字典、收集过程、组成、动机和预处理。该方法遵循结构化流程，使用登记表和评分标准来评估数据集的质量和完整性。将该方法应用于四个不同的数据集，揭示了各自的优势和改进空间。结果通过评分系统汇总，为增强数据集的透明度和完整性提供定制化建议。该评分卡兼顾技术与伦理层面，提供对数据实践的整体性评估。该方法旨在提升数据集质量，并为管理者和研究人员在开发负责任的AI系统方面提供实用指导，确保决策支持系统中的公平性与问责制。
英文摘要：
Artificial intelligence has transformed numerous industries, from healthcare to finance, enhancing decision-making through automated systems. However, the reliability of these systems is mainly dependent on the quality of the underlying datasets, raising ongoing concerns about transparency, accountability, and potential biases. This paper introduces a scorecard designed to evaluate the development of AI datasets, focusing on five key areas from the system card framework data development life cycle: data dictionary, collection process, composition, motivation, and pre-processing. The method follows a structured approach, using an intake form and scoring criteria to assess the quality and completeness of the data set. Applied to four diverse datasets, the methodology reveals strengths and improvement areas. The results are compiled using a scoring system that provides tailored recommendations to enhance the transparency and integrity of the data set. The scorecard addresses technical and ethical aspects, offering a holistic evaluation of data practices. This approach aims to improve the quality of the data set. It offers practical guidance to curators and researchers in developing responsible AI systems, ensuring fairness and accountability in decision support systems.
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。
