Assessing the Potential and Ethical Implications of Agentic AI in Surveillance Technology
Bibliographic record
Abstract
Agentic AI creates a system rather than a tool, an autonomous entity interacting with the world as humans do. By functionalizing agentic AI into surveillance technologies, we can increase surveillance systems' efficiency, accuracy, and scope, enabling them to monitor vast expanses of public space or issue, for example, a summary of dialogue from thousands of social media posts. However, it also interrogates the ethical dimensions of these systems, including the possible loss of privacy, accountability, and bias in decision making. AI surveillance technology is in use everywhere, but creating a surveillance state at the expense of civil rights, privacy, and freedom is a problem that can't be solved, no matter how many people are willing to use it. This paper proposes that despite the significant security operations improvements realized through agentic AI, the technology warrants an ethical framework with appropriate regulatory guardrails to manage the accompanying risks. Some points raised include the importance of transparency, fairness and accountability in using AI in surveillance settings, ensuring that these technologies are employed in a responsible and human rights compliant manner.
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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 | 0.074 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".