1. Department of Archaeology and Classical Studies, Wallenberglaboratoriet, Lilla Frescativägen 7, Stockholm University, 114 18, Stockholm, Sweden. firstname.lastname@example.org
2. Department of History, 400 Paterson Hall, 1125 Colonel By Drive, Carleton University, Ottawa, ON, K1S 5B6, Canada. CristinaWood@cmail.carleton.ca / ShawnGraham@cunet.carleton.ca
Cite this as: Huffer, D., Wood, C. and Graham, S. 2019 What the Machine Saw: some questions on the ethics of computer vision and machine learning to investigate human remains trafficking, Internet Archaeology 52. https://doi.org/10.11141/ia.52.5
This article represents the next step in our ongoing effort to understand the online human remains trade, how, why and where it exists on social media. It expands upon initial research to explore the 'rhetoric' and structure behind the use and manipulation of images and text by this collecting community, topics explored using Google Inception v.3, TensorFlow, etc. (Huffer and Graham 2017; 2018). This current research goes beyond that work to address the ethical and moral dilemmas that can confound the use of new technology to classify and sort thousands of images. The categories used to 'train' the machine are self-determined by the researchers, but to what extent can current image classifying methods be broken to create false positives or false negatives when attempting to classify images taken from social media sales records as either old authentic items or recent forgeries made using remains sourced from unknown locations? What potential do they have to be exploited by dealers or forgers as a way to 'authenticate the market'? Analysing the data obtained when 'scraping' image or text relevant to cultural property trafficking of any kind involves the use of machine learning and neural network analysis, the ethics of which are themselves complicated. Here, we discuss these issues around two case studies; the ongoing repatriation case of Abraham Ulrikab, and an example of what it looks like when the classifier is deliberately broken.
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