Goldsmiths - University of London

People's Councils for Ethical Machine Learning

McQuillan, Daniel. 2018. People's Councils for Ethical Machine Learning. Social Media and Society, ISSN 20563051 [Article] (In Press)

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Abstract or Description

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business and government. This paper will explore the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a ‘view from above’ whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgements, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this paper proposes people’s councils as a way to contest machinic judgements and reassert openness and discourse.

Item Type: Article


artificial intelligence machine learning ethics politics accountability justice people's councils

Departments, Centres and Research Units:



2 March 2018Accepted

Item ID:


Date Deposited:

13 Mar 2018 10:29

Last Modified:

13 Mar 2018 10:29

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI: http://research.gold.ac.uk/id/eprint/23040

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