Interoperable and standardized algorithmic images: The domestic war on drugs and mugshots within facial recognition technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Beginning in the 1990s, the National Institute of Standards and Technology (NIST) leveraged the 1980s’ American War on Drugs to improve and expand facial recognition technology (FRT) infrastructure, including the domestic building of FRTs reliant on mugshots. When examining mugshot databases gathered by the NIST, such as the Multiple Encounters Dataset (MEDS) I and II (2010) and Special Database 18 Mugshot Identification Database (SD-18) (2016), it is clear that the same gendered and racialized dynamics present in policing practices related to the War on Drugs is reflected in the mugshot databases that continue to use for FRT research and evaluation into the contemporary moment. This paper details the SD-18 and MEDS databases, as well as the MORPH database, showcasing how their representational, technical and political protocols operate. The desires for frictionless interoperability built into the images’ technical protocols supersede concerns for eugenic political and representational protocols, resulting in a current moment where the deployment of mugshot datasets cannot be contained to their original intended use with FRTs, but leak into other forms of algorithmic governance as well as into algorithmic image-making and visual culture, including generative artificial intelligence systems such as DALL-E.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it