Towards a chatbot for evidence gathering on the dark web
Bibliographic record
Abstract
We are underusing chatbots. Mainly, we seem to employ chatbots to a degree of use below this technology's current potential, both in its engineering capabilities and in terms of application areas. This may be due to our envisioned use of chatbots as replacing humans in a variety of service-oriented conversational tasks. Yet, even within this context of use, the decision to implement chatbots may be driven by financial or economic arguments, and their use is fairly conservative. In this provocation paper, we are arguing for a less conventional use of chatbots – that of intelligence-gathering agents operating on behalf of law enforcement on the dark web. This proposed use challenges both the current accepted uses of chatbots and their utilization that is not pushing the boundaries of technological capabilities. We discuss how chatbots may be used on the dark web and what sociotechnical challenges that may pose. Through this, we aim to demystify this example domain and instead see it as an opportunity to expand beyond the conventional implementation of chatbots.
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 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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".