algorithmic injustice

The unexpected transformations produced by the conjunction of COVID‐19, the murder of George Floyd and the resurgence of Black Lives Matter highlight the importance of social psychological understandings and the need for a step change in theorization of the social. This paper focuses on racialization. It considers issues that social psychology needs to address in order to reduce inequalities and promote social justice. It draws on theoretical resources of intersectionality and hauntology to illuminate the ways in which social psychological research frequently makes black people visible in ways that exclude them from normative constructions. The final main part of the paper presents an analysis of an interview with the racing driver Lewis Hamilton to illustrate possible ways of humanizing racialization by giving recognition to the multiplicity and historical location of racialized positioning. The paper argues that, while social psychology has made vital contributions to the understa...

Feminist data protection' is not an established term or field of study: data protection discourse is dominated by doctrinal legal and economic positions, and feminist perspectives are few and far between. This editorial introduction summarises a number of recent interventions in the broader fields of data sciences and surveillance studies, then turns to data protection itself and considers how it might be understood, critiqued and possibly reimagined in feminist terms. Finally, the authors return to 'feminist data protection' and the different directions in which it might be further developed-as a feminist approach to data protection, as the protection of feminist data, and as a feminist way of protecting data-and provide an overview of the papers included in the present special issue. Issue 4

This essay addresses the question of whether there are some types of AI that should never be built in the first place. The ‘Non-Deployment Argument’ has been subject to significant controversy recently: non-deployment skeptics fear that it will stifle innovation, and argue that the continued deployment and incremental optimization of AI tools will ultimately benefit everyone in society. I argue against the view that we should always try to build and optimize AI tools: making things better isn’t always good enough. In specific cases, there are overriding ethical and political reasons why we ought not to continue to build, deploy, and optimize specific AI tools. Instead, we should critically interrogate the value and purpose of using AI in a given domain in the first place.

The prediction of future states of the world is, probably, one of the most appealing perspectives opened up by the advent of Big data and artificial intelligence developments. This is also true in the legal field where a growing number of scholars and practitioners are drawn to the idea of exploiting the forecasting capabilities of algorithms. In recent years, prediction models have not only fed a lively theoretical debate on predictive justice and legal computability. They have also inspired the development of highly heterogeneous applications: new consulting services, intelligent platforms for workforce management, innovative tools for the judicial assessment of recidivism risk. In this scenario, while we are dazzled by the wonders of AI, a reflection is needed on the impact that computational heuristics can have on the very complexion of law. As a matter of fact, the use of predictive analytics in legal settings is often affected by issues spanning from inherent epistemic fragilities to the risk of turning into rights violations when dropped in real contexts. The paper provides a critical account of prediction and its hidden pitfalls by heading in two directions. The first one is to pave the way for an in-depth analysis of the theoretical and practical implications of predictive computation for the law. The second one is to present augmented intelligence – the cooperative integration between humans and machines – as a suitable paradigm to mitigate the risks of prediction and, more in general, to inspire the computational evolution of legal science and practice.

The prediction of future states of the world is, probably, one of the most appealing perspectives opened up by the advent of Big data and artificial intelligence developments. This is also true in the legal field where a growing number of scholars and practitioners are drawn to the idea of exploiting the forecasting capabilities of algorithms. In recent years, prediction models have not only fed a lively theoretical debate on predictive justice and legal computability. They have also inspired the development of highly heterogeneous applications: new consulting services, intelligent platforms for workforce management, innovative tools for the judicial assessment of recidivism risk. In this scenario, while we are dazzled by the wonders of AI, a reflection is needed on the impact that computational heuristics can have on the very complexion of law. As a matter of fact, the use of predictive analytics in legal settings is often affected by issues spanning from inherent epistemic fragilities to the risk of turning into rights violations when dropped in real contexts. The paper provides a critical account of prediction and its hidden pitfalls by heading in two directions. The first one is to pave the way for an in-depth analysis of the theoretical and practical implications of predictive computation for the law. The second one is to present augmented intelligence – the cooperative integration between humans and machines – as a suitable paradigm to mitigate the risks of prediction and, more in general, to inspire the computational evolution of legal science and practice.