In 2008, Dutch anti-Islam Member of Parliament Geert Wilders produced a short video called Fitna to visualize his argument that Islam is a dangerous religion. Thousands of men and women across the globe uploaded their own videos to YouTube to criticize or support the film. In this article, we look at these alternative videos from a feminist perspective, contrasting the gender portrayal and narratives in Fitna with those in the alternative videos. We contend that Fitna expressed an extremist Orientalist discourse, (...) in which women are presented as the current and future victims of the oppression of Muslim men and Islam. In contrast, the YouTube videos give voice to women themselves who come from across the globe, are relatively young and often active Muslims. Second, they express different view points in generically new ways, criticizing and ridiculing Wilders or producing serious and committed explanations of their own understanding of Islam. Third, although relatively few women appeared in the videos, those that did speak for themselves, not only take on Wilders, but also claim their right to speak within Islam. We propose to understand these videos as acts of citizenships through which women constitute themselves as global citizens, in some cases by engaging in ‘deliberation’ as it is understood in feminist political theory, in other cases by taking a ‘voice’ that can be responded to. (shrink)

This article presents Epistemic Spatialization as a new framework for investigating the interconnected patterns of biases when identifying objects with convolutional neural networks. It draws upon Foucault’s notion of spatialized knowledge to guide its method of enquiry. We argue that decisions involved in the creation of algorithms, alongside the labeling, ordering, presentation, and commercial prioritization of objects, together create a distorted “nomination of the visible”: they harden the visibility of some objects, make other objects excessively visible, and consign yet others (...) to permanent or haphazard invisibility. Our approach differs from those who focus on high-stakes misidentifications, such as errors tied to structural racism. Examining the far more dominant series of low-stakes mistakes shows the scope of errors, destabilizing the goal of image content identification with considerable societal impact. We explore these issues by closely examining the demonstration video of a popular convnet. This examination reveals an interlocking series of biases undermining the content identification process. The picture we paint is crucial for a better understanding of the errors that result as these convnets become further embedded in everyday products. The framework is valuable for critical work on computer vision, AI studies, and large-scale visual analysis. (shrink)