mlsanityData to 7 Oct 2026

Frankenstein in the Pipeline: Computational Epistemicide in Facial Recognition

arXiv 2606.07628 ↗First posted 30 May 2026cs.CY cs.CVcited by 0

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Abstract (arXiv)

While the eugenic roots of computer vision are well-documented in critical technology studies, less attention has been paid to the operational mechanisms through which this violence is enacted at the level of the pipeline. This paper employs Mary Shelley's Frankenstein not as a metaphor for unintended consequences, but as a diagnostic framework for method: disassembly, reconstruction, and the production of a creature whose legitimacy is asserted by the procedure that made it. I argue that embedding-based facial recognition enacts what I call computational epistemicide, an extension of Sueli Carneiro's concept of epistemicide to the computational domain - by destroying the face as a living, relational surface and authorizing a numerical proxy as the privileged site of identity. Across detection/cropping, landmarking, alignment/frontalization, and embedding, the face is progressively narrowed to what can be stabilized as data, producing a canonical face as the condition of legibility and a corresponding form-subject as the condition of recognition. Vectorization completes the Frankensteinian "stitching": the dissected face is reassembled into a fixed-dimensional artifact designed to circulate across databases and institutions. I then show how distance-based similarity and thresholding operationalize a norm of "close enough," making recognition inseparable from standardization and rendering reformist "ethical AI" optimization structurally insufficient. The paper concludes by arguing for abolition as a normative stance: refusing vectorized identity as a legitimate basis for rights and access, and dismantling the institutional impulse to govern human life through dissectible data points.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 45 references that are in the map. First posted on arXiv 30 May 2026.

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References · 7

  1. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2018Datasheets for Datasetscited by 1,371
  4. 2018Model Cards for Model Reportingcited by 1,068
  5. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  6. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  7. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380

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