Proceedings of the 38th Canadian Conference on Artificial Intelligence
Bibliographic record
Abstract
This paper explores the ethical challenges, particularly around Responsible AI, from the integration of large language models (LLMs) in generative AI (GenAI) applications across various domains. While LLMs enhance creativity, improve productivity, and enable human-like conversations, their opaque reasoning raises concerns about accountability and moral responsibility. The paper points out the limits of the existing framework of Meaningful Human Control (MHC), which emphasizes human oversight of AI systems. I argue that MHC alone is insufficient in addressing the challenges posed by LLMs. Instead, I suggest that MHC needs to be supplemented with virtue ethics and virtue epistemology, which focuses on exercising ‘acts of intellectual virtue’, such as open-mindedness, critical thinking, and epistemic humility. By integrating these frameworks, I propose a more holistic approach to Responsible GenAI to counteract the risks of automation complacency, to promote a more responsible, thoughtful engagement with GenAI, and ultimately to foster human flourishing and safeguard against the erosion of cognitive skills due to GenAI use.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".