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      What the Machine Saw: some questions on the ethics of computer vision and machine learning to investigate human remains trafficking

      1 , 2 , 2
      Internet Archaeology
      Council for British Archaeology

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          Abstract

          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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          Most cited references38

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          ImageNet classification with deep convolutional neural networks

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            Going deeper with convolutions

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              Deep learning in neural networks: An overview

              In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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                Author and article information

                Contributors
                Journal
                Internet Archaeology
                Internet Archaeol.
                Council for British Archaeology
                13635387
                March 14 2019
                Affiliations
                [1 ]Stockholm University
                [2 ]Carleton University
                Article
                10.11141/10.11141/ia.52.5
                eee7df52-1e67-49ef-b444-9c779902ac98
                © 2019

                http://intarch.ac.uk/includes/licence.txt

                History
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                Self URI (article page): http://intarch.ac.uk/journal/issue52/5/index.html

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