Author
Abstract
Research on the human microbiome has highlighted human-microbe interdependence, thus heralding a fundamental epistemic shift from a modernistic human individuality towards homo-microbial symbioticism. This shift is largely afforded by machine-learning algorithms that have also led to the popularization and commodification of this new episteme. Startup companies have accordingly begun developing microbiome-based AI-powered products, offering consumers access to personalized insights from their allegedly unique homo-microbial identities. Based on the case study of a successful startup that offers to algorithmically leverage people’s microbial states for personalized nutrition, we ask: How do users of microbiome-based personalized services perceive the homo-microbial identity? How do they interpret the algorithmic systems involved in these services? And how do these two epistemes—microbial and algorithmic—inform their understanding of their body, self, and agency? We show that such technologies promise consumers a “symbiotic singularity”—a unique, incommensurable homo-microbial identity that affords highly personalized recommendations. Nevertheless, while consumers initially accept this identity, they eventually “scale back” to a more conventional, modernistic view of themselves as humans, questioning both microbiome science’s symbioticism and its AI-powered singularity. Our findings highlight the implications of the commodification of microbiome science, reflecting broader concerns about the ways in which post-human epistemes are being embraced, proliferated, and commodified through the emerging field of data-driven personalized medicine and health solutions.
Suggested Citation
Rafi Grosglik & Dan M. Kotliar, 2024.
"Commodifying the microbial self: microbiome-based personalization and the quest for symbiotic singularity,"
Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
Handle:
RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-04209-5
DOI: 10.1057/s41599-024-04209-5
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