06/25/2025, 08:08 AM UTC
可解释的人工智能：针对目标群体透明化呈现结果Explainable AI: Making Results Understandable and Transparent for Target Groups
❶ 文章强调人工智能决策透明化的重要性，尤其在医疗诊断和招聘等领域，用户需理解AI输出的逻辑依据以建立信任并改进模型。
❷ 可解释AI（XAI）聚焦两大方向：为工程师提升数据/模型质量，以及满足伦理需求以提供面向用户的解释，确保AI的负责任应用。
❸ 白皮书建议推动XAI研究，标准化大型模型分析工具，将XAI纳入AI教育体系，并鼓励企业应用XAI以促进人类专业知识与机器学习的协同。
❶ The article emphasizes the importance of transparency in AI decision-making, particularly in fields like medical diagnostics and recruitment, where understanding the rationale behind AI outputs is critical for trust and model improvement.
❷ It highlights two main focuses of Explainable AI (XAI): enhancing data/model quality for engineers and addressing ethical requirements to provide user-centric explanations, ensuring responsible AI deployment.
❸ The whitepaper advocates advancing XAI research, standardizing tools for large-scale models, integrating XAI into AI education, and encouraging corporate adoption to foster collaboration between human expertise and machine learning.
---
本文由大语言模型（LLM）生成，旨在为读者提供半导体新闻内容的知识扩展（Beta）。
