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

Performance metrics alone cannot guarantee trustworthy AI systems. The paper emphasizes that despite age prediction models achieving high accuracy, "performance alone is insufficient" for understanding how these models actually work—a critical concern for high-stakes applications like healthcare and criminal justice where explainability is essential for accountability.

Current explainability methods struggle with complex medical data. The research identifies significant challenges in explaining age prediction models across two different modalities (EEG signals and lung X-rays), suggesting that existing XAI techniques may not adequately address the complexity of medical imaging and biometric data used in real-world applications.

We need specialized explanation approaches for different data types. By investigating explainability across two distinct modalities rather than treating all data the same way, the paper highlights that effective AI explanation requires tailored methods—one-size-fits-all approaches may fail to reveal how models make decisions from different types of medical information.

Transparency in high-impact domains requires ongoing research. The authors note that age prediction has "practical applications in high-impact fields like the healthcare domain or criminology," underscoring why understanding model decision-making in these areas is a responsibility, not optional, and why sharing models and techniques advances the field's ability to build trustworthy systems.