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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".