Gabriel Pereira

Algorithmic Antagonisms

Algorithmic Antagonisms: Resistance, Reconfiguration, and Renaissance for Computational Life

Call for Papers for Special Issue – Media International Australia

In August 2020, the UK government’s black-boxed algorithm for deciding students' grades made headlines. It had allegedly lowered the results of 40% of students, most of which from lower-income schools, thus crushing many students' hopes for entering prestigious universities. Students went out to the streets and protested, memorably chanting “Fuck the algorithm!”.

This recent case is just one of many that highlight a clear need for critical and empirical attention on algorithms and the work that they do, given their increasing importance in shaping social and economic life. There has been important work through critical studies that catalogue the multifaceted domination of algorithmic life and points of liberatory design out of it (Eubanks 2018, Costanza-Chock 2020), while recognising epistemological cleavages between powers of critique and scientific practice (Moats and Seaver 2019) in the seemingly impenetrable nature of “black boxed” algorithmic life (Pasquale 2015).

Much critical scholarship tied to algorithms focuses on the ills of algorithms, or the ways in which a normativity can be developed around an ethical, equitable or fair expression of computation via design (see ACM FAccT). Other responses include consideration of critical practices that advance data science in ways that identify and create social and organizational arrangements necessary for a more ethical data science (Neff et al. 2017) or move towards data justice (Dencik et al. 2019, Taylor 2017, Johnson 2014) to offer equity as design goals. Yet Critical Data Practices are also taking an antagonistic turn, focussing on ways to actively employ algorithms for everyday, social, and political agency, influence, or resistance.

This turn adjusts to reframe algorithms from governing black boxes to deployed tools that ‘mediate emerging distributions of power often too nascent [or] disconcerting to directly acknowledge’ (Thomas et al. 2018: 1). It considers posthumanist assemblages of humans, code, and technological artefacts that shape reality (Kalpokas 2019), but also includes what is missing from previous theorisation of algorithms – namely moving past acknowledging various normative relations to enabling a tactical use (Raley 2009).

That is to say, lessons from tactical media seem to be more applicable than ever in constantly shifting datalogical ground (Treré 2018; Velkova and Kaun 2019). To this end, this issue in part considers how critical histories of tactical media juxtapose structures of algorithmic life, and what might be done to leverage what was once dark towards antagonistic algorithmic light (Ochigame, 2020). Emergent examples reconfigure algorithms into networked media tools that act as vanguards against extant structures, with equity as a secondary concern. Algorithms are being deployed to radical and subersive action including automatically suing robocallers and contesting civil fines (DoNotPay); war crimes investigation via computer vision (VFrame, Forensic Architecture); gaming Google’s AdWords to point to sex-worker chat bots (Seattle Against Slavery); writing to MIDI all possible melodies and ‘releasing’ these through Creative Commons; or simply actively messing with Facebook’s feedback mechanisms to alter newsfeeds.

Hence, this issue asks: what are the ways in which algorithms are being deployed tactically to provocative ends? And, just as importantly, are these sustainable as activist or political practice? This issue will consider these trends and surrounding issues in order to introduce new ways of thinking about algorithmic politics in tactical and discrete terms. It hopes to open critical data and algorithm studies in ways that might reconfigure how critical scholarship approaches the algorithm in tactical terms as networked media tools that are antagonistic. We ask for submissions that consider the design of algorithms not as finished solutions that structure the world, but as something troubling - in a meaningful and helpful way - that might better inform our understanding of the capacities and limits of algorithmic life.

We are particularly looking forward to critical engagements with algorithmic practice, which may include feminist theory, de/post-colonial theory, critical race theory, queer theory, indigenous theory, perspectives from the Global South, and others.

The issue looks to submissions including but not limited to…

Proposed Timeline

28 February 2021: Abstracts (400-500 words) due for submission to guest editors

21 March 2021: Invitation to submit full papers sent to selected authors, with feedback on abstracts as applicable

31 July 2021: Full papers sent by authors for Peer Review

15 October 2021: Peer review returned to authors

(Up to) 30 Jan 2021: Final papers due for those papers that have passed/responded to review.

May 2022: Special Issue comes out on MIA

Editors: Luke Heemsbergen ([email protected]), Emiliano Treré ([email protected]), & Gabriel Pereira ([email protected])


Costanza-Chock, Sasha. (2020) “Design justice”, MIT Press.
Dencik, L., Hintz, A., Redden, J., & Treré, E. (2019). Exploring data justice: Conceptions, applications and directions.
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Johnson, J. A. (2014). From open data to information justice. Ethics and Information Technology, 16(4), 263-274.
Kalpokas I. (2019) Agency and the Posthuman Shape of Law. In: Algorithmic Governance. Palgrave Pivot, Cham
Moats, D., & Seaver, N. (2019). “You Social Scientists Love Mind Games”: Experimenting in the “divide” between data science and critical algorithm studies. Big Data & Society, 6(1), 2053951719833404.
Neff, G., Tanweer, A., Fiore-Gartland, B., & Osburn, L. (2017). Critique and contribute: A practice-based framework for improving critical data studies and data science. Big data, 5(2), 85-97.
Ochigame, R. (2020). Informatics of the Oppressed. Logic, 11. Retrieved from
Pasquale, F. (2015). The black box society. Harvard University Press.
Raley, R. (2009). Tactical media. U of Minnesota Press.
Thomas, S. L., Nafus, D., & Sherman, J. (2018). Algorithms as fetish: Faith and possibility in algorithmic work. Big Data & Society, 5(1).
Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2).
Treré, E. (2018). From digital activism to algorithmic resistance. In: Meikle, G. ed. The Routledge Companion to Media and Activism. London and New York: Routledge, pp. 367-375.
Velkova, J., & Kaun, A. (2019). Algorithmic resistance: media practices and the politics of repair. Information, Communication & Society, 1-18

Art for this CfP by Gabriel Pereira, inspired by Willys de Castro’s “Pintura M-111” (1956) and created with p5.js.

You can also read the CfP on the Media International Australia site.

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