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.
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.
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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".