Lifespan AI - Project C1: Explainable AI: Inference across the Lifespan

Lifespan AI - 项目 C1：可解释的 AI：整个生命周期的推理

• 批准号: 498749675
• 负责人: Professor Dr. Peter Maaß
• 金额: --
• 依托单位: Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS GmbH
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498749675?language=en
• 关键词: Lifespan; AI; Project; C1; Explainable

The recent rise of artificial intelligence (AI) in almost all research fields is closely followed by methods of explainable AI (XAI), which try to explain AI systems and black box machine learning models. These methods proved to perform well in many research fields, including applications on health data. For example, XAI methods are used to detect relevant pixel regions in medical imaging. However, in lifespan health data, we face heterogeneous input data with varying data structures and dimensions, longitudinal predictors measured over long periods of time and time-dependent outcomes. It is particularly challenging to integrate such data into AI systems and it is equally challenging to explain or interpret the resulting models. In this project, we tackle this challenge by developing XAI methods that can handle data covering the whole lifespan, including longitudinal predictors, time-dependent outcomes and combinations of different data sources, which change over the lifespan. We address both interpretation, i.e. understanding a model by itself, and explanation, i.e. using post-hoc methods on already trained models. For interpretation, we use invertible neural network structures, which directly relate the network's output with its input. For explanation, we develop methods based on attributions (e.g. gradient-based), attention mechanisms and loss-based methods. Further, we develop methods to generate so-called knockoffs for lifespan health data with normalizing flows, aiming at high dimensional conditional variable selection and conditional independence testing on such complex data. In summary, we aim at explaining and interpreting the AI models developed in the research unit. That is crucial for the research unit and, more generally, for health science because it contributes to the major goal of understanding health and disease mechanisms. It is also important for the general field of AI because our methodological advancements generalise beyond health data, e.g. to text or speech processing.

最近人工智能（AI）在几乎所有研究领域的兴起都紧随可解释人工智能（XAI）方法，它试图解释人工智能系统和黑盒机器学习模型。事实证明，这些方法在许多研究领域表现良好，包括健康数据的应用。例如，XAI 方法用于检测医学成像中的相关像素区域。然而，在生命周期健康数据中，我们面临着具有不同数据结构和维度的异构输入数据、长时间测量的纵向预测变量以及依赖于时间的结果。将这些数据集成到人工智能系统中尤其具有挑战性，解释或解释生成的模型也同样具有挑战性。在这个项目中，我们通过开发 XAI 方法来应对这一挑战，该方法可以处理覆盖整个生命周期的数据，包括纵向预测因子、时间相关结果以及随生命周期变化的不同数据源的组合。我们既解决解释问题，即理解模型本身，又解决解释问题，即在已经训练的模型上使用事后方法。为了进行解释，我们使用可逆神经网络结构，它将网络的输出与其输入直接相关。为了解释，我们开发了基于归因（例如基于梯度）、注意力机制和基于损失的方法的方法。此外，我们开发了通过标准化流程生成所谓的寿命健康数据仿制品的方法，旨在对此类复杂数据进行高维条件变量选择和条件独立性测试。总而言之，我们的目的是解释和解释研究单位开发的人工智能模型。这对于研究单位乃至健康科学至关重要，因为它有助于实现了解健康和疾病机制的主要目标。这对于人工智能的一般领域也很重要，因为我们的方法论进步超越了健康数据，例如。文本或语音处理。