Project Details
Projekt
Lifespan AI - Project C1: Explainable AI: Inference across the Lifespan
Applicants
Professor Dr. Peter Maaß; Professor Dr. Marvin Wright
Subject Area
Epidemiology and Medical Biometry/Statistics
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 459360854
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.
DFG Programme
Research Units
