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Out of their minds: legal theory in neural networks

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Abstract

This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.

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Hunter, D. Out of their minds: legal theory in neural networks. Artificial Intelligence and Law 7, 129–151 (1999). https://doi.org/10.1023/A:1008301122056

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