Beyond human: deep learning, explainability and representation

Fazi, M Beatrice (2021) Beyond human: deep learning, explainability and representation. Theory, Culture and Society, 38 (7-8). pp. 55-77. ISSN 0263-2764

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Abstract

This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence (AI) technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture tackle the possibility to ‘re-present’ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability (originally developed in the philosophy of science) to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic ‘thinking’ operations.

Item Type: Article
Keywords: algorithmic thought, deep neural networks, explanation, incommensurability, interpretability, philosophy, XAI
Schools and Departments: School of Media, Arts and Humanities > Media and Film
Research Centres and Groups: Sussex Humanities Lab
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 03 Nov 2020 11:15
Last Modified: 07 Apr 2022 12:45
URI: http://sro.sussex.ac.uk/id/eprint/94780

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