During the last years we have entered a new phase in the application of artificial intelligence (AI) in archaeology. The main advances within machine learning (ML) and deep learning (DL) have been successfully implemented in multiple archaeological case studies. However, it is easy to see that the use of these new methods comes with its own set of problems, and lack of a common procedure and standardization.
The variety of applications includes sites detection and material culture analysis, showing that these methods are able to define a wide spectrum of socio-economic aspects of the societies, such as the individual preferences of craftsmen, the technological mindset of the communities, or their exchange of ideas beyond any geographical borders.
At this session, in order to integrate the archaeological ML research into general archaeological practice, we would like to welcome every researcher who struggles with the above problems and also:
• All case studies on the application of AI, especially with the use of new algorithms and approaches to different sources of archaeological information with a clear focus on improvements.
• Analyses of how ML and DL have been implemented in archaeological research with a clear focus on the issues that have arisen and those studies that have proposed solutions to these issues. Plans for the use of these methods and the barriers researchers are encountering.
• Best practices and procedures, which can include comparative analysis, of how to approach the most common issues in archaeological research, such as the small amount of training data available.
• Ethical issues in ML-based archaeological research with a particular focus on the growth that AI has had globally during the last year.