Challenges facing the explainability of age prediction models: case study for two modalities

• URL: http://arxiv.org/abs/2303.06640v1
• Date: Sun, 12 Mar 2023 11:51:21 GMT
• Title: Challenges facing the explainability of age prediction models: case study for two modalities
• Authors: Mikolaj Spytek, Weronika Hryniewska-Guzik, Jaroslaw Zygierewicz, Jacek Rogala, Przemyslaw Biecek
• Abstract summary: 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.
• Score: 4.000351859705655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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