Principles for the socially responsible use of conservation monitoring technology and data
Bibliographic record
Abstract
Abstract Wildlife conservation and research benefits enormously from automated and interconnected monitoring tools. Some of these tools, such as drones, remote cameras, and social media, can collect data on humans, either accidentally or deliberately. They can therefore be thought of as conservation surveillance technologies (CSTs). There is increasing evidence that CSTs, and the data they yield, can have both positive and negative impacts on people, raising ethical questions about how to use them responsibly. CST use may accelerate because of the COVID‐19 pandemic, adding urgency to addressing these ethical challenges. We propose a provisional set of principles for the responsible use of such tools and their data: (a) recognize and acknowledge CSTs can have social impacts; (b) deploy CSTs based on necessity and proportionality relative to the conservation problem; (c) evaluate all potential impacts of CSTs on people; (d) engage with and seek consent from people who may be observed and/or affected by CSTs; (e) build transparency and accountability into CST use; (f) respect peoples' rights and vulnerabilities; and (g) protect data in order to safeguard privacy. These principles require testing and could conceivably benefit conservation efforts, especially through inclusion of people likely to be affected by CSTs.
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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 | 0.252 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.094 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.024 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".