Dr Tracie Farrell

Justice can be viewed as "objective" or mediated through power [Chomsky & Foucault, 1971; Costanza-Chock, 2018]. Finding commonalities across different legal and ethical frameworks [Floridi & Cowls, 2019; Jobin et al., 2019] is an example of the former. In the latter, justice is a "requirement" for non-equitable societies, ensuring protection for the most harmed [Cugueró-Escofet & Fortin, 2014]. The difficulty in achieving this type of justice through A.I. is that A.I. is used primarily for classification and prediction [Vinuesa et al., 2020]. Growing evidence indicates that A.I. accelerates and compounds social bias, contributing to unequal distributions of power [O'Neil, 2016, p. 3, Noble, 2018; Benjamin]. "Trade-offs" in providing accurate and fair predictions also impact sub-populations disproportionately [Yu et al. 2020], meaning that people with multiple forms of marginalisation are more likely to be misunderstood by A.I. than those with normative characteristics [Costanza-Chock, 2018]. While there are legal and ethical frameworks that should govern the way we use A.I., minority voices are still under-represented [Buolamwini, J. and Gebru, T., 2018, Costanza-Chock, 2018; Magalhães & Couldry, 2020] and there are few structures for enforcement and accountability [Mittelstadt, 2019]. We need to rethink how A.I. is contributing to justice as a relational concept, which includes dimensions of power and marginalisation. This project draws together the cultural, technical, and socio-technical expertise necessary to extend our current notions of justice in empirical research for A.I. for social good (AI4SG). To start with, a team of 3 researchers will develop a conceptual model of A.I. and "justice" that includes a) different definitions of justice used to frame the tasks of A.I. and evaluate their efficacy, b) the questions that can be answered under that definition and c) the trade-offs that are determined to be acceptable in the process. The research team will map scholarly literature from AI4SG to the ethical, legal or political frameworks that underpin the research, identifying gaps or conflicts in how justice is operationalised within AI4SG in comparison with other social justice models. In particular, we will explore the questions: are different positions on justice incompatible with A.I.? Can we identify new pathways for justice to emerge? To extend our conceptual model, we will conduct 3 case studies in which minority interests are ignored within specific A.I. tasks: 1) non-binary people in gender-based analysis of sexism 2) discriminatory deplatforming of sex workers or artists through content moderation and 3) shadow-banning activists as part of a counter-terrorism approach. The case studies will explore conflicts between these communities' concept of justice and the A.I. task, and which alternative solutions exist. They will also contribute to the global problem of tackling online harm and using A.I. techniques to help identify and classify relevant cases. Finally, to test alternative solutions, a multi-sectoral Advisory Board of A.I. and community experts will be brought together to create a design challenge for A.I. researchers. Issued through 2 workshops at top-level A.I. conferences, the challenge will be to prioritise marginalised perspectives. The outputs of the challenge and their evaluation will inform a set of guidelines for dealing with errors and trade-offs in AI4SG. Our contribution is to a) expose connections between how A.I. researchers define justice and which justice questions we attend to in AI4SG; b) reflect on the benefits of A.I. for which societies; and c) influence and inspire researchers to question assumptions of A.I. research around acceptable trade-offs and errors. This research will bring together social scientists, community experts and A.I. researchers to explore what new lines of inquiry can be opened by focusing on maximising the benefits in A.I. for marginalised groups.