Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils

Archaeologists have long puzzled over who made Upper Paleolithic hand stencils, since no ground truth exists and prehistoric hands differ from modern ones. This paper tackles that uncertainty head-on with a deep learning pipeline that generates multiple silhouette interpretations per stencil, then runs them through two neural network ensembles trained on thousands of contemporary hand images. By combining this with manifold mapping and explainable AI, the system doesn't just guess sex --- it flags how confident that guess is. Applications include rock art archaeology, anthropology, and any forensic-style problem where degraded imagery demands uncertainty-aware classification rather than false certainty. Authors: Karel Becerra, Boris Mederos, Dean Snow, Ramón A. Mollineda Paper: https://arxiv.org/abs/2608.14539v1