People's Councils for Ethical Machine Learning

McQuillan, Daniel. 2018. People's Councils for Ethical Machine Learning. Social Media + Society, 4(2), [Article]

Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

Download (119kB) | Preview
2056305118768303.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (111kB) | Preview

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:


Identification Number (DOI):


artificial intelligence, machine learning, ethics, politics, accountability, Big Data, Hannah Arendt justice people's councils

Departments, Centres and Research Units:



2 March 2018Accepted
2 May 2018Published Online
1 April 2018Published

Item ID:


Date Deposited:

13 Mar 2018 10:29

Last Modified:

04 Jun 2020 13:36

Peer Reviewed:

Yes, this version has been peer-reviewed.


View statistics for this item...

Edit Record Edit Record (login required)