Interdisciplinary Dialogues: The Major Risks of Generative AI
Bibliographic record
Abstract
In an exciting series of Interdisciplinary Dialogues on the societal impacts of AI, we invite a guest speaker and panellists from the fields of science and engineering, health and humanities and social sciences to discuss the advances, challenges and opportunities raised by AI. The first dialogue in this series began with Yoshua Bengio, who, concerned about developments in generative AI and the major risks they pose for society, initiated the organization of a conference on the subject. The event took place on August 14, 2023 in Montreal, and was aimed at initiating collective, interdisciplinary reflection on the issues and risks posed by recent developments in AI. The conference took the form of a panel, moderated by Juliette Powell, to which seven specialists were invited who cover a variety of disciplines, including: computer science (Yoshua Bengio and Golnoosh Farnadi), law (Caroline Lequesne and Claire Boine), philosophy (Jocelyn Maclure), communication (Sonja Solomun) and political science (Hugo Loiseau). This document is the result of this first interdisciplinary dialogue on the societal impacts of AI. The speakers were invited to respond concisely, in the language of their choice, to questions raised during the event. Immerse yourself in reading these fascinating conversations, presented in a Q&A format that transcends disciplinary boundaries. The aim of these dialogues is to offer a critical and diverse perspective on the impact of AI on our everchanging world.
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 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 | 0.108 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.031 | 0.073 |
| Scholarly communication | 0.031 | 0.046 |
| Open science | 0.006 | 0.044 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".