Artificial Intelligence and the Law: New Challenges and Possibilities for Fundamental Human Rights and Security - Panel 1
Bibliographic record
Abstract
Allan Hutchinson Reflections on Singularity: AI and Law’s Multiplicity\nJon Penney How Safe Are AI Safety Standards?\nCarys Craig The AI-Copyright Trap\nValerio De Stefano Artificial Intelligence and Work\nAida Abraha Examining AI Governance in the Workplace Context: A Comparative Analysis of Workplace Technology Regulations in Canada, the United States, and the European Union.\nFrançois Tanguay-Renaud, Contrasting Police Powers of Detention and Arrest in Canada and the United States: Is There a Place for Predictive AI and Some Thoughts about Racial Profiling and its Regulation\nArtificial Intelligence (AI) is dramatically reshaping how people live, work, and interact, as well as the functioning of societies and legal systems’ adaptations to these changes. Machine learning technologies’ integration into various decision-making processes carries profound implications for sentencing, taxation, workplace dynamics, surveillance and policing, privacy, and financial markets. The rising automation of human activities prompts significant legal inquiries spanning constitutional, contractual, and tort issues. Large Language Models (LLMs) such as Chat GPT are AI technologies with a range of legal, ethical, and societal implications. These models, trained on massive volumes of text data, can generate text resembling human language, enabling tasks like answering questions, writing essays, even crafting poetry. They implicate freedom of expression, the right to information, and the democratic process at large. They have the potential to generate misleading, harmful, or hateful content, regardless of their programmers’ and owners’ intentions. They could become tools for propaganda or disinformation campaigns. They raise intellectual property questions, particularly when their output is based on pre-existing intellectual or artistic works and could lead to mass job automation.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.020 | 0.035 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.017 | 0.030 |
| Insufficient payload (model declined to judge) | 0.014 | 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".