When Biometrics Fail: Gender, Race and the Technology of Identity
Bibliographic record
Abstract
WHEN BIOMETRICS FAIL: GENDER, RACE AND THE TECHNOLOGY OF IDENTITY Shoshana Amielle Magnet Durham: Duke University Press, 2011 Building upon a well-established tradition of considering science and technology as constituted by culture, Shoshana Amielle Magnet problematizes the discourses behind the expansion of biometrics--technologies that aim at verification and identification by means of using data obtained from measuring bodies through iris and retina scans, digital fingerprinting, and facial recognition. Marketed as perfect tools to reduce human error and eliminate subjective judgement, digital biometrics are being increasingly implemented in the areas of law enforcement, information access, and border security. Magnet, however, calls into question the industry's claims of impartiality of identification technologies by arguing that biometrics are based on outdated, essentialized notions of identity and disproportionately target minority populations. The notion of biometric failure features centrally in the book, and Magnet unpacks its multiple meanings in the introductory chapters. In a literal sense, identification and verification technologies fail more often than the biometrics industry representatives would like to admit: there are mismatches and false rejections of known subjects as well as possibilities that high-tech devices can be hacked or fooled. On a larger level, biometrics fail to realize their core promises of objectivity, convenience, and reliability. Magnet adopts Donna Haraway's concept of corporeal fetishism to explain how a relentless pursuit to uncover the inner truth of identity aims at transforming a body into a knowable, fixed object. The framework of corporeal fetishism allows Magnet to trace how bodies that do not conform to a projected image of a white, able-bodied, and gender-conforming male user are constructed as inscrutable and therefore, as having a low economic value in a big business of biometrics. To support her argument, Magnets cites numerous accounts of biometric failures on othered bodies. For example, face scanners sometimes fail to accurately identify people of color; iris scanners are not designed to accommodate individuals with visual impairments; devices that speed up the flow of passengers in the airport will not work on people in wheelchairs or with certain medical conditions. Biometric systems not only privilege white able bodies, but also assume a strict male/female binary which erases the existence of gender-variant individuals. After providing an overview of the development of biometric technologies, Magnet critically assesses the three major areas of their use: the prison industrial complex and the welfare system in the U.S., and the security system at the U.S.-Canada border. Operating in a neo-liberal context of moving from rehabilitation to punishment, prisons function as locations of surveillance, allowing biometrics companies to capitalize on the growing rates of incarceration; with no opportunity to opt out, prisoners become convenient test subjects for identification and verification technologies. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".