Towards Responsible Ecosystem Service Management Using Artificial Intelligence
Bibliographic record
Abstract
The increasing integration of artificial intelligence (AI) into environmental monitoring and management presents both promising opportunities and complex ethical challenges. While AI offers the potential to enhance the efficiency, accuracy, and scope of environmental data collection and analysis, it also raises concerns about data privacy, algorithmic bias, transparency, and accountability. This chapter explores the ethical dimensions of AI in environmental science, focusing on a case study of deep learning models for predicting Escherichia coli (E. coli) levels at recreational beaches along the northern shore of Lake Erie, the boundary between Canada (Ontario) to the north and the United States (Michigan, Ohio, Pennsylvania, and New York) to the west, south, and east. The study highlights the challenges of predicting rare but critical events, such as unsafe swimming conditions, and the potential for biased data to lead to inaccurate predictions with significant public health implications. By analysing the case study and drawing on real-world examples, the chapter illuminates the ethical considerations that must guide the development and deployment of AI in environmental monitoring and management. It emphasises the need for data quality, model transparency, human oversight, and continuous learning to ensure that AI is used responsibly and effectively to protect public health and promote a sustainable future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".