Technology Producers use of Language and Discourse to Shape and Reinstate Anti-Black Global Realities: An Analysis of Amazon’s Facial Recognition Technology Communications and Responses to Racial Bias in Rekognition
Bibliographic record
Abstract
As of 2016, one in two American adults could be found in at least one American law enforcement face recognition network (Garvie, Bedoya & Frankle, 2016). Racial bias in facial recognition technology is an important site of study as technology is largely conceived by public and state actors as neutral and democratic in nature, exempt from the biases and prejudices of human life (Noble, 2018). This study will trace the ways in which Amazon’s responses to claims of racial bias in Rekognition and FRT general descriptions allow race and existing relations of power to manifest and persist. This study employs a critical discourse analysis to argue that Amazon works to obscure racial bias in both development and application of FRTs in law enforcement. Amazon also enables what I refer to as discourses of racial neutrality which, allows Amazon to deem any racially biased FRT outcomes as a "glitch in the system" that has nothing to do with race, despite decades of evidence proving otherwise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".