让AI决策可解释，提升高风险领域信任度。
Transparent AI: The Case for Interpretability and Explainability
- 将可解释性作为设计核心，而非事后补救。
- 总结跨领域实践中的可操作策略。
- 适合正在推进AI落地的企业与研究机构。
随着人工智能系统在各领域日益影响高风险决策，透明性已成为负责任、可信赖AI应用的基础。依托本机构在推动AI研究和促进产业应用方面的领先地位，本文总结了在多个不同领域中实际应用可解释性的关键洞察与经验教训。论文提供了针对不同AI成熟度组织的可操作策略与实施指导，强调将可解释性融入系统设计的核心原则，而非作为事后补充。通过实际案例分析，展示了如何在保障性能的同时增强模型决策的透明度，为组织在复杂环境中部署可信AI提供实践路径。
原文摘要 · Abstract (English)
As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation. Leveraging our role as a leading institute in advancing AI research and enabling industry adoption, we present key insights and lessons learned from practical interpretability applications across diverse domains. This paper offers actionable strategies and implementation guidance tailored to organizations at varying stages of AI maturity, emphasizing the integration of interpretability as a core design principle rather than a retrospective add-on.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。