Frankenstein in the Pipeline: Computational Epistemicide in Facial Recognition

Technical Summary: Frankenstein in the Pipeline: Computational Epistemicide in Facial Recognition

Problem Statement
The paper argues that the primary harm of facial recognition technology (FRT) is not merely the downstream performance disparities (e.g., biased error rates across race and gender) often cited in critical literature, but an ontological and operational violence inherent to the pipeline itself. The core problem is that contemporary embedding-based FRT systems enact "computational epistemicide." This process systematically destroys the face as a living, relational surface and replaces it with a numerical proxy (a vector) authorized as the legitimate site of identity. By reducing the face to a fixed-dimensional artifact through a sequence of epistemic operations, the system renders the living subject subordinate to its own data representation, stripping the face of its historical, situated, and relational meanings.

Methodology
The paper employs a novel diagnostic framework derived from Mary Shelley's Frankenstein, not as a metaphor for unintended consequences, but as a methodological tool for analyzing the pipeline's architecture. The author maps the novel's logic of disassembly and reassembly onto the technical stages of FRT:

1. Disassembly: The face is broken down into measurable components (detection, landmarking).
2. Reconstruction: These components are stitched together into a standardized artifact (embedding/vector).
3. Constitutive Failure: The resulting "creature" (the vector) is treated as the authoritative truth of the subject, despite the fact that the method of its production required the destruction of the subject's original relational context.

The analysis proceeds by tracing the technical pipeline stage-by-stage (Capture → Detection/Cropping → Landmarking → Alignment/Frontalization → Embedding → Comparison/Thresholding) to demonstrate how each step functions as an epistemic operation that discards specific forms of knowledge (context, micro-expressions, temporal variation) to retain only what is computable. The paper situates this technical analysis within the broader genealogy of eugenics and Sueli Carneiro's concept of epistemicide (the production of "non-being" through the denial of epistemic authority).

Key Contributions
The paper makes two primary contributions:

1. Operationalization of Computational Epistemicide: It defines and traces the specific sequence of epistemic operations in FRT that transform a living face into a governable proxy. It argues that the violence is not an accident of biased data but is constitutive of the method: the face is made knowable only by being reduced to what the system can stabilize as data.
2. Modeling the Politics of Similarity: It analyzes how identity is operationalized as "distance" in a learned geometry. The paper demonstrates that similarity is not a neutral metric but a normative apparatus. By converting continuous proximity into a binary decision via thresholding, the system enforces a norm of "close enough," making recognition inseparable from standardization.

Results and Technical Analysis
The paper details the progressive reduction of data through the pipeline:

• Reduction Ratio: A surveillance scene (~2 million pixel values) is compressed through detection (5 landmarks), alignment (12,544 pixels), and embedding (512 floats) to a single scalar decision. The compression ratio from aligned face to vector is approximately 4,000:1.
• Canonicalization: The alignment and frontalization stages enforce a "canonical face"—a geometric and photometric discipline that determines which variations count as signal and which are treated as noise. This produces a "form-subject" that can only be recognized if it conforms to this template.
• Vectorization: The embedding step completes the substitution, creating a fixed-dimensional artifact designed to circulate across databases. This vector is optimized for comparison, not for describing the face's relational reality.
• Thresholding: The decision boundary converts metric proximity into institutional authority. The threshold operationalizes a policy on which errors are acceptable, effectively naturalizing the conversion of facial comparability into social truth.

The paper notes that even if performance disparities were eliminated (i.e., if the system were equally accurate across all demographics), the harm would persist because the system would still require the ontological substitution of the living face with a vector.

Significance and Claims
The paper claims that reformist approaches to "Ethical AI"—which focus on optimizing accuracy, calibrating thresholds, or diversifying datasets—are structurally insufficient. These approaches treat the harm as a failure of execution rather than a failure of the representational method itself. Because the violence is enacted by the method of proxy-production (dissection and stitching), improving the proxy does not resolve the epistemic displacement.

Consequently, the paper argues for abolition as a normative stance. This is not a call for better recognition or fairer surveillance, but a refusal of the premise that any institution should possess the authority to convert human faces into governable data points. The paper posits that rights and access should not be conditional on the production of a metric self. Instead, it advocates for institutional alternatives (such as self-sovereign identity or community-based verification) that do not require the computational dissection of the face. The significance of this work lies in shifting the critical focus from the outcomes of algorithmic governance to the philosophy of decision embedded within the technical architecture itself.