AI and the falling sky: interrogating X-Risk
Bibliographic record
Abstract
AbstractThis paper argues that the headline-grabbing nature of existential risk (X-Risk) diverts attention away from immediate artificial intelligence (AI) threats, including fairly disseminating AI risks and benefits and justly transitioning towards AI-centred societies. Section I introduces a working definition of X-Risk, considers its likelihood and explores possible subtexts. It highlights conflicts of interest that arise when tech luminaries lead ethics debates in the public square. Section II flags AI ethics concerns brushed aside by focusing on X-Risk, including AI existential benefits (X-Benefits), non-AI X-Risk and AI harms occurring now. Taking the entire landscape of X-Risk into account requires considering how big risks compare, combine and rank relative to one another. As we transition towards more AI-centred societies, which we, the authors, would like to be fair, we urge embedding fairness in the transition process, especially with respect to groups historically disadvantaged and marginalised. Section III concludes by proposing a wide-angle lens that takes X-Risk seriously alongside other urgent ethics concerns.
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 | 0.053 | 0.077 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.008 |
| 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; both teacher heads agree on what is shown here.
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".