Explainable Multimodal AI for Adaptive Calibration of Archaeological Sensing Workflows

AI can enhance rather than replace human expertise: The framework operates as an additional quality-control layer alongside traditional instrument calibration, rather than replacing expert judgment. This demonstrates how AI can support archaeologists' existing workflows and decision-making rather than automating away professional knowledge.

Explainability is essential for high-stakes applications: The paper emphasizes explainable AI (XAI) techniques to help users understand why the system flags data as problematic—identifying specific issues like "illumination inconsistencies" or "detector instability." This transparency is crucial when AI systems influence decisions about valuable archaeological artifacts and research data.

Multimodal AI requires careful integration: The framework combines four different sensing technologies (3D imaging, hyperspectral imaging, X-ray fluorescence, and Raman spectroscopy) through a unified system. This shows that responsible AI development for specialized domains must account for how different data types interact and validate quality across multiple measurement methods simultaneously.

Responsible AI includes resource efficiency: The system supports "resource-aware acquisition strategies," meaning it helps archaeologists make better decisions about when to collect more data versus when current data is sufficient. This reflects responsible AI that considers practical constraints and avoids wasteful data collection.

Anomaly detection protects data integrity: By identifying degradation patterns before data is used in downstream analysis, the system prevents poor-quality measurements from corrupting research findings—a critical safeguard for ensuring the reliability of archaeological conclusions.