Predict or Create: Discussing the Different Understandings of the Future in the Context of AI
Bibliographic record
Abstract
This symposium explores how different conceptions of the future influence the development and application of artificial intelligence (AI). Based on the theoretical foundation of understanding the future as either predictable or unpredictable, the symposium aims to discuss the impact of AI in creating and/or predicting the future. While AI tools that use machine learning and neural networks to draw inferences from data sets are seen as having great potential to predict futures, there are also conflicting views on the accuracy of algorithmic predictions, with some emphasizing the limitations of past data in predicting the unknown. The symposium aims to move beyond binary perspectives and advocate for a nuanced understanding of the sociomaterial interactions between humans and AI in constructing futures. Examples such as Amazon's predictive shipping, where AI combines predicting and creating future purchases, will be presented as examples of this middle ground. The symposium invites scholars to contribute to a research agenda that explores further examples of human-AI collaboration in creating and predicting the future and promotes a more comprehensive understanding of the role of AI in future shaping processes.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".