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Koninklijk Nederlands Lucht en Ruimtevaartcentrum logo Koninklijk Nederlands Lucht- en Ruimtevaartcentrum Homepage Koninklijk Nederlands Lucht en Ruimtevaartcentrum logo Koninklijk Nederlands Lucht- en Ruimtevaartcentrum Homepage Internship: Explainable AI for the qualification of aerospace algorithms - Hybrid - - Amsterdam , Noord-Holland , Netherlands - Aerospace Systems Background Machine learning is increasingly used in aerospace for tasks that have traditionally relied on human experts or on fixed rule-based procedures. For such tasks, data-driven algorithms promise gains in speed, consistency and sensitivity. Before they can be used in practice however, they must be qualified, which means it has to be demonstrated with sufficient substantiation that the algorithm performs its intended function reliably within a defined set of conditions. The qualification approaches used in the sector today were developed for human operators and for conventional systems whose behaviour can be traced back to explicit requirements. They do not translate directly to algorithms whose behaviour is learned from data, and emerging guidance from aviation authorities names explainability as a key ingredient of trustworthy AI, while leaving open how it should be demonstrated in practice. Performance figures on a test set do not show why an algorithm reaches a decision, whether it relies on physically meaningful information, or how it behaves outside the data it was trained on. Explainable machine learning offers a wide range of methods to address this, from feature attribution and saliency methods, to example-based and concept-based explanations, but it is still an open question which of these can provide evidence that is strong enough to support qualification. This project therefore investigates how explainability methods can contribute to the qualification of machine learning algorithms for aerospace, starting from an use case, and provides a ...

Koninklijk Nederlands Lucht en Ruimtevaartcentrum 标志 Koninklijk Nederlands Lucht- en Ruimtevaartcentrum 主页 Koninklijk Nederlands Lucht en Ruimtevaartcentrum 标志 Koninklijk Nederlands Lucht- en Ruimtevaartcentrum 主页 实习：用于航空航天算法鉴定的可解释 AI - 混合 - - 阿姆斯特丹，北荷兰省，荷兰 - 航空航天系统 背景 机器学习在航空航天领域越来越多地用于传统上依赖人类专家或固定规则程序的任务。对于此类任务，数据驱动算法有望在速度、一致性和灵敏度方面带来提升。然而，在它们能够投入实际使用之前，必须对其进行鉴定，这意味着必须以充分的依据证明该算法在既定条件范围内可靠地执行其预期功能。该行业目前使用的鉴定方法是为人类操作员和传统系统开发的，这些系统的行为可以追溯到明确的需求。这些方法不能直接适用于从数据中学习行为的算法，而航空当局新兴的指导文件将可解释性列为可信 AI 的关键要素，但同时未说明在实践中应如何证明这一点。测试集上的性能数据并不能表明算法为何做出某项决策、它是否依赖具有物理意义的信息，或者它在训练数据之外的行为如何。可解释机器学习提供了广泛的方法来应对这一问题，从特征归因和显著性方法，到基于示例和基于概念的解释，但其中哪些方法能够提供足以支持鉴定的证据，仍是一个未解的问题。因此，本项目从某个用例出发，研究可解释性方法如何为航空航天机器学习算法的鉴定作出贡献，并提供一种……

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