Deep automation of inequality as a self-sustaining and self-fulfilling process: The example of binarism and sexism

Datafication is simultaneously becoming a means and a goal of modern marketdriven uses of technologies. The optimisation of the “consumer experience” shapes a data-driven consumer subject, which does not encompass all practices and identities. The article aims to problematise datafication, which overlooks complex “citizen experiences” that also include gender and sexual identity. At the same time, former and existing gender inequalities remain fundamental learning data, based on which algorithms and artificial intelligence generate models of future sociality. We encounter at least three interconnected types of inequality (on the levels of culture, data and technologies) that can create a self-sustaining and self-learning process that reproduces future inequalities.