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Subverting machines‚ fluctuating identities: Re−learning human categorization

Christina Lu‚ Jackie Kay and Kevin McKee

Abstract

Most machine learning systems that interact with humans construct some notion of a person’s “identity,” yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static. In stark contrast, strands of thought within critical theory present a conception of identity as malleable and constructed entirely through interaction; a doing rather than a being. In this work, we distill some of these ideas for machine learning practitioners and introduce a theory of identity as autopoiesis, circular processes of formation and function. We argue that the default paradigm of identity used by the field immobilizes existing identity categories and the power differentials that co-occur, due to the absence of iterative feedback to our models. This includes a critique of emergent AI fairness practices that continue to impose the default paradigm. Finally, we apply our theory to sketch approaches to autopoietic identity through multilevel optimization and relational learning. While these ideas raise many open questions, we imagine the possibilities of machines that are capable of expressing human identity as a relationship perpetually in flux.

Book Title
Proceedings of the 2022 ACM Conference on Fairness‚ Accountability‚ and Transparency
ISBN
9781450393522
Keywords
algorithmic fairness‚ identity systems‚ social construction‚ theories of identity
Location
Seoul‚ Republic of Korea
Pages
1005–1015
Publisher
Association for Computing Machinery
Series
FAccT '22
Year
2022