Potential for near-term AI risks to evolve into existential threats in healthcare
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
The recent emergence of foundation model-based chatbots, such as ChatGPT (OpenAI, San Francisco, CA, USA), has showcased remarkable language mastery and intuitive comprehension capabilities. Despite significant efforts to identify and address the near-term risks associated with artificial intelligence (AI), our understanding of the existential threats they pose remains limited. Near-term risks stem from AI that already exist or are under active development with a clear trajectory towards deployment. Existential risks of AI can be an extension of the near-term risks studied by the fairness, accountability, transparency and ethics community, and are characterised by a potential to threaten humanity's long-term potential. In this paper, we delve into the ways AI can give rise to existential harm and explore potential risk mitigation strategies. This involves further investigation of critical domains, including AI alignment, overtrust in AI, AI safety, open-sourcing, the implications of AI to healthcare and the broader societal risks.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it