  This chapter argues for a structural injustice approach to the governance of
AI. Structural injustice has an analytical and an evaluative component. The
analytical component consists of structural explanations that are well-known in
the social sciences. The evaluative component is a theory of justice.
Structural injustice is a powerful conceptual tool that allows researchers and
practitioners to identify, articulate, and perhaps even anticipate, AI biases.
The chapter begins with an example of racial bias in AI that arises from
structural injustice. The chapter then presents the concept of structural
injustice as introduced by the philosopher Iris Marion Young. The chapter
moreover argues that structural injustice is well suited as an approach to the
governance of AI and compares this approach to alternative approaches that
start from analyses of harms and benefits or from value statements. The chapter
suggests that structural injustice provides methodological and normative
foundations for the values and concerns of Diversity, Equity, and Inclusion.
The chapter closes with an outlook onto the idea of structure and on
responsibility. The idea of a structure is central to justice. An open
theoretical research question is to what extent AI is itself part of the
structure of society. Finally, the practice of responsibility is central to
structural injustice. Even if they cannot be held responsible for the existence
of structural injustice, every individual and every organization has some
responsibility to address structural injustice going forward.
