Challenges facing the explainability of age prediction models: case study for two modalities
This addresses the need for explainability in age prediction models for high-impact fields like healthcare and criminology, but it is incremental as it focuses on applying existing XAI methods to specific data types.
The paper tackles the problem of understanding how age prediction models work by applying Explainable AI (XAI) to two modalities, EEG signals and lung X-rays, and shares predictive models to enable further research on explanation techniques.
The prediction of age is a challenging task with various practical applications in high-impact fields like the healthcare domain or criminology. Despite the growing number of models and their increasing performance, we still know little about how these models work. Numerous examples of failures of AI systems show that performance alone is insufficient, thus, new methods are needed to explore and explain the reasons for the model's predictions. In this paper, we investigate the use of Explainable Artificial Intelligence (XAI) for age prediction focusing on two specific modalities, EEG signal and lung X-rays. We share predictive models for age to facilitate further research on new techniques to explain models for these modalities.