Algorithmic Bias and Data Injustice: Dark Side or Dark Matter?

This panel brings together five contributions that, impinging on the notions of algorithmic bias and data injustice, explore both the dynamics producing data-induced harm and the manifestations of such harm on people. Ranging from data-based treatment of LGBTQ+ communities, to algorithmic bias in e-government and exclusion of recipients from datafied food security systems, the panel engages the debate on whether the notion of a ‘dark side’, widely applied to the adverse side effects of information systems, is appropriate to discuss data-induced unfairness. As an alternative framing, the panel introduces the notion of a ‘dark matter’ of datafied systems, where bias and injustice are designed into the technology. The panel aims at generating debate on unfairness with the view of imagining fairer data-based technologies, and thus contributing to building a future where a ‘force for good’ can effectively stem from datafication.