用多个模型解释结果聚合，提升AI决策可信度。
Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness
- 融合k近邻、随机森林和神经网络的特征重要性
- 多模型聚合使解释更稳定，信任度显著提升
- 适合高风险场景下需要可靠解释的AI系统
人工智能在真实世界高风险应用中的普及，引发了对其可信度与伦理使用的广泛讨论。可解释人工智能（XAI）通过揭示复杂黑箱模型的决策过程来应对这一挑战。然而，解释的鲁棒性常被忽视——只有稳健的解释方法才能真正增强系统整体信任。本文通过融合k近邻、随机森林和神经网络的特征重要性聚合，探索其对解释鲁棒性的提升效果。初步结果表明，利用多个模型的预测能力进行解释聚合，能有效增强应用的可信度。
原文摘要 · Abstract (English)
The use of Artificial Intelligence (AI) models in real-world and high-risk applications has intensified the discussion about their trustworthiness and ethical usage, from both a technical and a legislative perspective. The field of eXplainable Artificial Intelligence (XAI) addresses this challenge by proposing explanations that bring to light the decision-making processes of complex black-box models. Despite being an essential property, the robustness of explanations is often an overlooked aspect during development: only robust explanation methods can increase the trust in the system as a whole. This paper investigates the role of robustness through the usage of a feature importance aggregation derived from multiple models ($k$-nearest neighbours, random forest and neural networks). Preliminary results showcase the potential in increasing the trustworthiness of the application, while leveraging multiple model's predictive power.
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