解释AI决策过程，让航空航天专家信任并用好AI系统
The Role of XAI in Transforming Aeronautics and Aerospace Systems
- 梳理XAI定义与目标，明确可解释性标准
- 总结透明模型特性及训练后解释技术
- 展示XAI在航空航天领域的实际应用场景
人工智能（AI）的最新进展已深刻改变航空与航天领域的决策方式。然而，这些智能系统生成的预测结果往往缺乏透明度，亟需理解其决策依据，尤其对行业专业人士而言至关重要。在此背景下，可解释人工智能（XAI）应运而生，有效弥合了专业人员与复杂AI系统之间的认知鸿沟。本文系统回顾了XAI的概念，明确定义其核心目标，并探讨了其中涉及的模型类型及其应具备的透明性特征。同时，文章分析了训练后用于解析AI行为的后处理解释技术。最后，通过多个应用案例，展示了XAI在航空与航天领域如何助力专业人士理解模型运行机制，提升系统的可信度与可用性。
原文摘要 · Abstract (English)
Recent advancements in Artificial Intelligence (AI) have transformed decision-making in aeronautics and aerospace. These advancements in AI have brought with them the need to understand the reasons behind the predictions generated by AI systems and models, particularly by professionals in these sectors. In this context, the emergence of eXplainable Artificial Intelligence (XAI) has helped bridge the gap between professionals in the aeronautical and aerospace sectors and the AI systems and models they work with. For this reason, this paper provides a review of the concept of XAI is carried out defining the term and the objectives it aims to achieve. Additionally, the paper discusses the types of models defined within it and the properties these models must fulfill to be considered transparent, as well as the post-hoc techniques used to understand AI systems and models after their training. Finally, various application areas within the aeronautical and aerospace sectors will be presented, highlighting how XAI is used in these fields to help professionals understand the functioning of AI systems and models.
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