Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
English summary
The paper introduces Evaluation Cards, a structured interpretive layer designed to make AI evaluation reports more accessible by distilling complex metrics into clear summaries. It addresses the common problem of technical jargon and opaque data that often obscure meaningful insights from stakeholders. The cards enhance transparency and enable developers, researchers, and end-users to better understand AI system strengths and weaknesses. This approach aims to improve trust, accountability, and collaborative decision-making around AI technologies.
Chinese summary
该论文提出了评估卡（Evaluation Cards），一种结构化的解释层，通过将复杂的评估指标精炼为清晰的摘要，使AI评估报告更易于理解。它解决了技术术语和晦涩数据常使利益相关者难以获取有意义见解的问题。这些卡片增强了透明度，帮助开发者、研究人员和终端用户更好地理解AI系统的优缺点，从而提升对AI技术的信任、问责和协作决策。
Key points
Proposes Evaluation Cards as a structured format to summarize AI evaluation results in an accessible, interpretive layer.
提出评估卡作为一种结构化格式，以可访问的解释层总结AI评估结果。
Targets the gap between complex evaluation metrics and user comprehension, reducing reliance on technical jargon.
针对复杂评估指标与用户理解之间的鸿沟，减少对技术术语的依赖。
Aims to increase transparency, trust, and accountability by enabling clearer communication of AI performance to diverse stakeholders.
旨在通过更清晰地向不同利益相关者传达AI性能，提高透明度、信任度和问责制。