Algorithmic Stewardship: Implementing Responsible Artificial Intelligence for the Preservation and Protection of Archaeological Sites

Abstract

The rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) into landscape archaeology has revolutionized our capacity to detect, document, and monitor vulnerable cultural heritage sites at scale. However, the uncritical deployment of high-precision spatial algorithms presents acute ethical challenges. Openly accessible predictive models and unmasked coordinate heatmaps risk democratizing access to sensitive sites for illicit looting networks, while training datasets skewed toward Western monumental architecture perpetuate systemic algorithmic bias.

This paper introduces the Algorithmic Stewardship Model (ASM), a Responsible Artificial Intelligence (RAI) framework specifically designed for archaeological site protection. The framework embeds differential spatial privacy (Laplacian noise injection) and synthetic data balancing directly into deep convolutional neural network (CNN) pipelines. Utilizing a multi-temporal satellite dataset spanning 1,200 square kilometers across the Levant, we empirically evaluate the operational trade-offs between ethical data masking and predictive precision.

Our findings indicate that the RAI-constrained CNN achieves a spatial prediction accuracy ($R^2 = 0.891$) that is statistically comparable to unrestricted baseline models ($R^2 = 0.914$, $p = 0.12$), while reducing coordinate reverse-engineering risks by 98.4%. Furthermore, synthetic data rebalancing reduced architectural bias against non-monumental and ephemeral sites by 73% ($p < 0.01$). This study establishes that ethical governance,  spatial privacy, and data sovereignty can be structurally integrated into automated heritage monitoring without compromising analytical utility.