Danaher, John and Hogan, Michael J. and Noone, Chris and Kennedy, Rónán and Behan, Anthony and de Paor, Aisling and Felzmann, Heike and Haklay, Muki and Khoo, Su-Ming and Murphy, Maria Helen and O'Brolchain, Niall and Schafer, Burkhard and Shankar, Kalpana (2017) Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4 (2). pp. 1-21. ISSN 2053-9517

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Abstract

We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a) barriers to legitimate and effective algorithmic governance and (b) the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence.

Item Type:	Article
Keywords:	Algorithmic governance; Big Data; algocracy; collective intelligence; interactive management; public participation;
Academic Unit:	Faculty of Social Sciences > Law Faculty of Social Sciences > Research Institutes > Maynooth University Social Sciences Institute, MUSSI
Item ID:	11679
Identification Number:	https://doi.org/10.1177/2053951717726554
Depositing User:	Maria Murphy
Date Deposited:	12 Nov 2019 14:18
Journal or Publication Title:	Big Data & Society
Publisher:	Sage Publications
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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