Regulating Artificial Intelligence for Social Impact
Bibliographic record
Abstract
Abstract In the context of increasing global competition and the need to find efficient and rapid solutions to be successful in this process (at private business or state level), factors such as knowledge, technology, innovation and more recently Artificial Intelligence (AI) are considered elements that can be exploited in order to create competitive advantages and increase the added value for the participants. The aim of this chapter is to examine the opportunities and challenges that AI creates, in the context of the increasing need for regulation to supervise the development and deployment of AI in different sectors. The increasing adoption of AI has already produced significant transformations in many sectors: education, healthcare, finance, manufacturing, transportation by increasing efficiency, improving innovation and facilitating service delivery. At the same time, these advances have raised questions related to solutions for job replacement, data privacy, fairness and societal inequalities. In this chapter, the authors aim to highlight how different countries have regulated AI and the differences between general regulations on AI, such as the EU AI Act, and the sector-specific regulations prevalent in some countries, such as the United States, Japan, China or Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".