Computing Culture; a humanities approach to artificial neural networks

Kieran Browne — Thesis Proposal Review

The problem

Semantics derived automatically from language corpora necessarily contain human biases

Caliskan, Aylin, Joanna J Bryson, and Arvind Narayanan. (2017).

The nightmare scenario

Explaining visually

Explaining visually

Explaining visually


Johanna Drucker

Humanities Approaches to Graphical Display

Rethinking our approach to visualization and the assumptions that underpin it

  • Constructivist notions of data
  • Representing ambiguity
  • Designing for interpretation and sense-making

The way we use and understand neural networks is built on realist models of knowledge

Proposed methods

  • Critique current visual language of neural networks with care for the assumptions they bring.
  • Design experimental visualisations and interfaces to explore the possibilities of visual representation


Artificial neural networks are a potent technology capable of extracting and reproducing complex patterns from data, including cultural, linguistic and visual processes previously beyond the reach of computation. However, the internal functions of neural networks are notoriously difficult to understand. The researchers and technicians who design and train neural networks can rarely explain their behaviour. This is particularly problematic in cases where neural networks learn, and thus perpetuate cultural biases. My research inherits its theoretical framework from the (digital) humanities, particularly the ideas of Johanna Drucker regarding graphical display and constructionist notions of data. I intend to make use of mixed methods in my research. This includes critical analysis of existing representations of neural networks and practice-led research, specifically developing experimental visualisations and novel exploratory interfaces. - introduction - what's a neural network - what's a black box - why is the neural network a black box - the black box getting darker - semantics - we want our explanations in terms of meaningful concepts - extracting semantics - problems extracting semantics - marked and unmarked categories - networks often uncritically applied to cultural expressive forms - why is this a problem - examples of cultural bias in neural networks - computer science is illequipped handle questions of culture and bias - code needs criticism - scope - - theory - Johanna Drucker - humanities approaches to (graphical display/ interface theory) - data vs capta - precedents (why visualisation) - knowledge as visually mediated - when you have a hammer everything looks like a nail - attempts to visualise neural networks with techniques for data vis and graphical user interfaces; but neural networks are not data and not much like normal digital tech - methodologies - practice led research - research through making - interfaces - visualisations - critical artworks - critical approach to neural networks - critical approach to data (CAPTA) - redescribing a neural networks as cultral computing - neural networks as cultural actors - training as enculturing - what are the semantics of internal representations - originality - related work