Power and Resistance in the Twitter Bias Discourse

In 2020, the saliency-based image cropping tool deployed by Twitter to generate image previews was suspected of carrying a racial bias: Twitter users complained that Black people were systematically cropped out and, thus, made invisible by the cropping tool. As a response, Twitter conducted bias analyses, concluded that the cropping tool was indeed biased, and subsequently removed it. Soon after, Twitter hosted the first "algorithmic bias bounty challenge", inviting the general public to detect algorithmic harm in the cropping tool.

Twitter’s image cropping algorithm is a fascinating case study for exploring the push-and-pull dynamics of power relations between, firstly, algorithmic knowledge production inherent in machine learning systems, secondly, the bias discourse as resistance, and, thirdly, ensuing corporate responses as stabilization measures towards said resistance. In order to account for this three-part narrative of the case study, this paper is structured along the examination of the following three questions: (1) How is algorithmic, and especially, data-based knowledge production entrenched in power relations? (2) In what way does the discourse around bias serve as a vehicle for resistance against said power? Why and in what way is it effective? (3) How did Twitter as a company stabilize its position within and in relation to the bias discourse?

This paper explores these questions along the following steps: Section 2 lays out the interdisciplinary theoretical perspective of the analysis, combining, firstly, a mathematical-epistemic perspective that examines the mathematics underlying both machine learning systems and bias analyses with, secondly, Foucauldian concepts that make it possible to view mathematical tools as articulations of power relations. The subsequent three sections engage with the three questions posed above: Section 3, Power, is concerned with the first question, and it focuses on the algorithmic knowledge production in relation to Twitter’s cropping tool and its mathematical-epistemic foundations. Section 4, Resistance, addresses the second question, and it examines three bias analyses of the cropping tool, as well as their epistemic limitations, and it continues by conceptualizing the bias discourse in academic scholarship and activism as resistance to power. Section 5, Stabilization, engages with the third question, discussing Twitter’s response to the bias accusations and the way in which the company was able to effectively stabilize its position – rendering the bias discourse a vehicle for counter-resistance, too. This paper will be published in the open access volume Algorithmic Regimes: Methods, Interactions, and Politics (Amsterdam University Press, forthcoming), as well as on SSRN as a preprint.