This article discusses the process for creating a dataset to train and test the core and the corrector AI classifiers of the Arch-I-Scan Project. This dataset includes the photographs of real sherds that were compiled by the project from the collections of our project partners as well as synthetic sherds that were generated to supplement the low numbers of real sherd training data and to account for the imbalances among the numbers of real sherds per class (i.e., per vessel form). This article presents the simulation process used to create the synthetic sherds, as well as the process we used to decrease the differences between images of real and simulated sherds so that they were useable together for training the machine. Finally, the compilation and size of the resulting overall dataset are outlined.

This article presents analyses that have been carried out on the data compiled by the Arch-I-Scan Project. The project is ultimately concerned with using Roman tablewares for consumption-oriented analyses, particularly for investigating socio-cultural behaviour around eating and drinking. In addressing this concern, not only are the forms of such tableware vessels important but also their actual and relative sizes. This article, therefore, used rim diameters of different forms of terra sigillata vessels, to analyse the size ranges of each form and to assesses evidence of differences in the sizes of each of these vessel forms across three different locations – Leicester, London and Vindolanda. It also assesses whether there are any evident patterns in the sizes and size ranges of the different traditional morphological, or shape, categories, here called 'use labels' (i.e., so-called bowls, plates and dishes), among these locations. It speculates on what these might tell us about differences in the eating and drinking practices among these locations. Finally, it presents a process for the automation of rim diameter measurement from photographs of these vessel remains, attempting to deal with the evident variation in how recorders have physically measured rim diameters. We also discuss the issues faced during this automated rim diameter measurement process, whether using rim sherds from our image dataset or taking further specific photographs of rim sherds (e.g., blur in the images, lens distortion), and provide suggestions for more reliably automating rim diameter measurement.

In this article we introduce the process by which we developed, trained and tested an AI classifier with the aim that it can identify forms of terra sigillata vessels from photographs of sherds of this material. We highlight the problems we faced with this identification process before focusing on how these have been addressed. AI systems are not perfect and can sometimes make mistakes. This can pose even greater problems when such systems are used blindly in fields like archaeology where the level of uncertainty is particularly high. Despite augmenting our training dataset with simulated images to counteract our low numbers of images of real sherds, the classifier still did not exhibit a very high performance. Many of its mistakes were no doubt due to it being forced to generate an output despite being uncertain of its decision. In this article we introduce a new approach to handling such uncertainty that uses weakly supervised error correctors that help catch errors without requiring potentially large amounts of additional training data, are easy to construct, and are computationally fast at runtime. In contrast to direct approaches to correcting AI errors whereby an AI correcting algorithm seeks to replace the original AI decision with an alternative, our correctors aim to detect instances of potentially risky decisions, where the probability of making an erroneous decision is high. In such instances our correctors instruct the AI system to avoid making such high-risk decisions altogether. We show how the proposed AI correctors can be deployed to improve the performance of the baseline AI without expensive model retraining. The development of a system for the complexity of archaeological data is an excellent illustration of the general efficiency and practical utility of such an approach.

High-dimensional tabular data are common in biomedical and clinical research, yet conventional machine learning methods often struggle in such settings due to data scarcity, feature redundancy, and limited generalization. In this study, we systematically evaluate Synolitic Graph Neural Networks (SGNNs), a framework that transforms high-dimensional samples into sample-specific graphs by training ensembles of low-dimensional pairwise classifiers and analyzing the resulting graph structure with Graph Neural Networks. We benchmark convolution-based (GCN) and attention-based (GATv2) models across 15 UCI datasets under two training regimes: a foundation setting that concatenates all datasets and a dataset-specific setting with macro-averaged evaluation. We further assess cross-dataset transfer, robustness to limited training data, feature redundancy, and computational efficiency, and extend the analysis to a real-world ovarian cancer proteomics dataset. The results show that topology-aware node feature augmentation provides the dominant performance gains across all regimes. In the foundation setting, GATv2 achieves an ROC-AUC of up to 92.22 (GCN: 91.22), substantially outperforming XGBoost (86.05), $\alpha =0.001$. In the dataset-specific regime, GATv2, combined with minimum-connectivity filtering, achieves a macro ROC-AUC of 83.12, compared to 80.28 for XGBoost. Leave-one-dataset-out evaluation confirms cross-domain transfer, with an ROC-AUC of up to 81.99. SGNNs maintain ROC-AUC around 85% with as little as 10% of the training data and consistently outperform XGBoost in more extreme low-data regimes, $\alpha =0.001$. On ovarian cancer proteomics data, foundation training improves both predictive performance and stability. Efficiency analysis shows that graph filtering substantially reduces training time, inference latency, and memory usage without compromising accuracy. Overall, these findings suggest that SGNNs provide a robust and scalable approach for learning from high-dimensional, heterogeneous tabular data, particularly in biomedical settings with limited sample sizes.

Data-driven artificial intelligence (AI) systems, particularly neural networks, have demonstrated significant success across various applications. The increasing availability of digital imaging, sensor technology, and computational modelling has positioned data-driven AI as a transformative methodological tool for archaeology. Automated systems based on deep learning can support the recognition and classification of artefacts from photographic or 3D data at an unprecedented scale. However, commonly-used networks may fail to deliver high-quality decisions for datasets such as the Arch-I-Scan Project’s chosen dataset, comprising 2D photographs of fragmentary ceramic remains. This is due to such datasets’ low-sample properties and the various limitations of each artefact (e.g. archaeological artefacts are usually incomplete, the extant fragments have hugely varying geometries, and, at least for ceramic vessels, appropriate type labelling is often scarce) resulting in high uncertainty of identification of the specific type of the original artefact. The deployment of data-driven AI in archaeology and dealing with such complex, high-dimensional data is, therefore, accompanied by theoretical, computational, and epistemological challenges. This article outlines recent work on synthetic data generation, error correction, and the mathematics of AI stability and dimensionality, and outlines the main advantages and limitations of using AI for the classification of archaeological artefacts through photographic images of them. Our experience in carrying out this project highlights the importance of advancing mathematical theory and methods to improve AI models' stability, accuracy, and verifiability in high-dimensional data tasks.