<i>Person of Interest</i> as Media Technology of Surveillance: A Cautionary Tale for the Future of the National Security State With Diegetic Big Data Surveillance, Algorithmic Security, and Artificial Intelligence
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
In this article, we examine the television series about global surveillance Person of Interest ( POI) (aired on CBS, 2011–2016) to reflect on popular understandings of surveillance and account for its representation of artificial intelligence (AI) and algorithmic surveillance. Drawing on television, media, and surveillance studies, we focus on the power of representations of social relations and diegetic technologies in possible and imagined futures to explore the role of cultural representations in shaping social order. Through the character of the AI Machine representing algorithmic surveillance, we evaluate the show’s critique of algorithmic autonomy and contend that, as a media technology of surveillance, the show participates in the hype of big data as a panacea while banalizing surveillance. We argue that POI could facilitate a comprehensive analysis of the politics of algorithmic surveillance but fails to do so due to its uncritical representation of artificial intelligence agencies in detecting security risks.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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