"Down to Earth": Design Considerations for AI for Sustainability from the Environmental and Climate Movement
Bibliographic record
Abstract
As the Earth's temperature continues to rise, increasing investments are being made to develop artificial intelligence (AI) technologies to address the current climate crisis.Through interviewing 19 participants-comprising climate and environmental advocates and developers of AI for sustainability in the US and Canada-we examine how advocates perceive and use these technologies, and how their perspectives converge and diverge from practitioners developing AI for sustainability.We identified three key findings: 1) while approaches differ, developers and advocates expressed care for people and the planet; 2) the developers' and advocates' values and perceptions of AI technology varied, especially around ethical issues; and 3) developers and advocates had distinct approaches to using and designing AI and digital tools.Our findings, guided by a climate justice lens, underscore the need for decision-makers to: engage with advocates from intended beneficiary communities early in the design process; prioritize the urgency of the climate crisis; and emphasize the tangible environmental and societal impact of digital systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".