Generative AI and Social Media May Exacerbate the Climate Crisis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The contributions of generative artificial intelligence (AI) and social media to the climate crisis are often underestimated. To date, much of the focus has been on direct emissions associated with the life cycle of tech products. In this forum article, we argue that this narrow focus misses the adverse and indirect impacts of generative AI and social media on the climate. We outline some of the indirect ways in which generative AI and social media undermine the optimism, focus, creativity, and veracity required to address the climate crisis. Our aim is twofold. First, we seek to balance the tide of optimism about the role of digitalization in addressing the climate crisis by offering a skeptic’s perspective. Second, we outline a new research agenda that moves beyond counting directly attributable carbon emissions and proposes a more comprehensive accounting of the indirect ways in which social media and generative AI adversely impact the sociopolitical conditions required to address the climate crisis.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it