Browne, Jude. AI and Structural Injustice, A Feminist Perspective
2023, In Browne, J., et al. (eds.), Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines. Oxford University Press
, Contributed by: Marilou Niedda
Abstract:
In this chapter, I draw on the seminal work of feminist theorist Iris Marion Young to consider how political responses to structural injustice, exacerbated by AI,¹ might be developed. My interpretation of Young’s work is centred on what I see as an overlooked feature of her account of structural injustice - ‘untraceability’. Drawing on Arendt’s concept of ‘thoughtlessness’ and the work of other scholars such as Benjamin, I explore the relationship between structural injustice and algorithmic decision making,² which, itself, is becoming increasingly untraceable. I argue that while fault-based regulation of AI is vital for combating injustice exacerbated by AI, too little thought goes into addressing the structural dynamics of AI’s impact on society which requires a different sort of approach. I conclude by offering some suggestions on how we might think about mitigating the exacerbation of structural injustice posed by AI. In particular, I advocate adopting several elements of the mini-public approach within the regulatory public-body landscape of democratic governance systems to form a new ‘AI Public Body’ (using the UK as my example).
In this chapter, I draw on the seminal work of feminist theorist Iris Marion Young to consider how political responses to structural injustice, exacerbated by AI,¹ might be developed. My interpretation of Young’s work is centred on what I see as an overlooked feature of her account of structural injustice - ‘untraceability’. Drawing on Arendt’s concept of ‘thoughtlessness’ and the work of other scholars such as Benjamin, I explore the relationship between structural injustice and algorithmic decision making,² which, itself, is becoming increasingly untraceable. I argue that while fault-based regulation of AI is vital for combating injustice exacerbated by AI, too little thought goes into addressing the structural dynamics of AI’s impact on society which requires a different sort of approach. I conclude by offering some suggestions on how we might think about mitigating the exacerbation of structural injustice posed by AI. In particular, I advocate adopting several elements of the mini-public approach within the regulatory public-body landscape of democratic governance systems to form a new ‘AI Public Body’ (using the UK as my example).
Comment: On the one hand, this book chapter is useful for introducing the concept of structural injustice to students by examining the (un)traceable responsibility that individuals bear when they unconsciously (re)produce structural inequities. The text is fairly easy to read, especially to apprehend such a central concept in feminist theory of justice. On the other hand, Browne applies her understanding of traceable liability to AI technologies, which is an application that students can understand quite well. It also brings about a social perspective in the philosophy of AI.