Practice, Theory, and Ethics of Machine Learning in Archaeology

Part of: Society for American Archaeology 90th Annual Meeting, Denver, CO (2025)

This collection contains the abstracts of the papers presented in the session entitled "Practice, Theory, and Ethics of Machine Learning in Archaeology" at the 90th annual meeting of the Society for American Archaeology.

Technological advances have catalyzed scientific innovation and societal change for centuries, but the recent precipitous rise in computing power has introduced powerful new tools at a rapid pace, which can be overwhelming to parse. In particular, the increasing popularity of machine learning (ML) methods in archaeology has occurred so quickly that many scholars are left with questions regarding how, why, and with what datasets these methods should be used. This symposium explores the practices, theories, and ethics linked to emerging ML methods in archaeology. We showcase new and innovative approaches to the topic, explore the practical applications of ML that emphasize enhancing data quality, site preservation, and synthesis, discuss publishing and code sharing, and provide a forum to discuss the ethical use of ML in archaeology.

Other Keywords
digital archaeology • Worldwide

Resources Inside This Collection (Viewing 1-8 of 8)

  • Documents (8)
Documents
  • Advancing Machine Learning Approaches to Identifying Charcoal Morphologies and Fuels for Sedimentary Charcoal Analysis (2025)
    DOCUMENT Citation Only Grant Snitker.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. Differentiating between natural and anthropogenic fire in the past remains one of the principal challenges in interpreting paleo-charcoal records and has implications for contextualizing changing fire regimes in our world today. During the Holocene, cultural burning practices throughout the globe were motivated by diverse...

  • Enhancing Petrographic Analysis with Convolutional Neural Networks (2025)
    DOCUMENT Citation Only Ashley Rutkoski.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. Archaeological research has highlighted the role of mollusks in coastal communities' foodways, construction practices, and cultural traditions, but its use within pottery production has received less attention. Key morphological and chemical signatures are altered during pottery manufacture, impeding identification of...

  • Fake it till you make it: Deep learning detection of archaeological features using simulated training data (2025)
    DOCUMENT Citation Only Katherine Peck.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. High resolution digital surface datasets have become increasingly accessible over the last two decades. Archaeologists have responded by developing methods to streamline locating archaeological sites in these data at a landscape scale. As high-powered computing hardware and cloud computing solutions improve, deep learning...

  • Investigating Geospatial Arrangements of Stone Knapping at a Maya Lowland Site Using Random Forest Modeling (2025)
    DOCUMENT Citation Only Amy Rieth.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. The machine learning algorithm Random Forest has proven highly accurate in classifying archaeological soil and lithic microdebitage particles. Understanding this efficacy, this model was selected for implementation on soil samples collected from the market plaza of the Late Classic Maya site of Tzikin Tzakan. The ultimate...

  • Making Machine Learning More Accessible and Useful in Archaeology: Insights from Chronology Building (2025)
    DOCUMENT Citation Only C. Michael Barton.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. While machine learning is beginning to appear in the archaeological literature, most archaeologists remain unfamiliar with this potentially useful analytical toolkit. Over the past decade, we have been exploring machine learning as a robust way to help address a significant challenge of the archaeological record:...

  • A multigenerational workflow: Applying Deep Learning tools on old maps to detect near-invisible historic sites. (2025)
    DOCUMENT Citation Only Claudine Gravel-Miguel.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. While archaeologists aim to use the latest technology to detect, classify, or analyze archaeological sites, they still face the classic problem that some sites are simply no longer visible due to soil deposition and erosion. While satellite imagery and aerial LiDAR data can sometimes help us see the outlines of certain...

  • Tracking the exposure of geoglyphs after Amazon deforestation bouts using deep learning of satellite imagery (2025)
    DOCUMENT Citation Only Jonathan Paige.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. The Amazon rainforest contains an abundant record of human occupation, including evidence of extensive landscape modification, and construction of extensive earthworks and roads. However, reconstructing the extent and precise location of archaeological sites is impractical without analyzing either LiDAR imagery of forested...

  • Transferable object detection approaches in archaeology for both terrestrial- and underwater-based projects (2025)
    DOCUMENT Citation Only Leila Character.

    This is an abstract from the "Practice, Theory, and Ethics of Machine Learning in Archaeology" session, at the 90th annual meeting of the Society for American Archaeology. This talk will focus on deep learning approaches to object detection in archaeology using remotely sensed data. We will discuss several case studies that use similar methodological approaches, presenting shared conclusions drawn from across the case studies. Case studies will include two terrestrial projects focused on...