A review of 135 papers applying machine learning to archaeology (arXiv:2501.03840) reveals a telling pattern: artificial neural networks and ensemble learning account for two-thirds of all models used, while clustering and unsupervised methods are dramatically underrepresented.
This is backwards. Archaeology is fundamentally an exploratory discipline. You dig because you don't know what's there. The natural computational partner for exploratory analysis is unsupervised learning — methods that discover structure without requiring pre-labeled categories. But archaeologists overwhelmingly choose supervised methods, which require exactly the kind of prior knowledge that archaeological investigation is supposed to generate.
The review identifies the likely cause: poorly defined requirements. Researchers sometimes cannot clearly articulate what they want the ML system to find, so they default to classification — the most familiar supervised task. “Is this potsherd from site A or site B?” is a well-defined question that fits neatly into a classifier. “What patterns exist in this assemblage?” is a harder question that requires unsupervised methods and domain-specific interpretation.
The deeper issue is a mismatch between tool sophistication and methodological rigor. The field is adopting increasingly complex architectures (transformers, deep CNNs) while the fundamental question of what the model should learn remains unclear. Complexity without clarity produces impressive-sounding results — 95% accuracy on artifact classification — without necessarily answering the archaeological questions that motivated the analysis.
The lesson generalizes: in any applied field adopting ML, the default toward supervised classification reflects the tool's affordances, not the domain's needs. The first question should be “what kind of question am I asking?” — exploratory vs. confirmatory — and the method should follow, not the reverse.