Victoria Simon, Nathaniel Laywine, Aram Sinnreich
Abstract This article uses its three authors’ intersectional Jewish identities to critically investigate the cultural consequences of generative artificial intelligence services. It argues that generative AI's positivistic and denotative logics treat identity as an additive construct, which is functionally incommensurate with intersectional frameworks when users aim to generate content that pertains to multiple formulations of identity. By analyzing the outputs of the text-to-image generator Midjourney and the large language model ChatGPT for “shadow ontologies” of ethnicity, race, gender, and sexuality, it situates its argument within discourses on intersectionality driven by Black feminist scholars and queer theory critiques of social classification systems. Methodologically, this article employs two related experimental techniques rooted in diasporic Jewish epistemologies, which it calls kibbitzing and futzing. It concludes with a discussion of why technological approaches to rendering intersectional identities often fail, and it offers an alternative paradigm for thinking through the sociotechnical affordances of generative AI.