Abstract

This paper discusses various forms and sources of algorithmic discrimination. In particular, we explore the connection between – at first glance – ‘voluntary’ shar- ing or selling of one’s data on the one hand and potential risks of automated decision-making based on big data and artificial intelligence on the other. We argue that the implementation of algorithm-driven profiling or decision-making mechanisms will, in many cases, disproportionately disadvantage certain vul- nerable groups that are already disadvantaged by many existing datafication practices. We call into question the voluntariness of these mechanisms, especially for certain vulnerable groups, and claim that members of such groups are often- times more likely to give away their data. If these existing datafication practices exacerbate prior disadvantages, they ‘compound historical injustices’ (Hellman, 2018) and thereby constitute forms of morally wrong discrimination. To make matters worse, they are even more prone to further algorithmic discriminations based on the additional data collected from them.