Global AI policies and international business
Bibliographic record
Abstract
How best to make sure that AI systems used in business and society are safe, transparent, traceable, non-discriminatory, and environmentally compatible is rapidly becoming a public policy priority. The European Union’s Parliament, for example, has tried to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems. The institution defines AI as “the ability of a machine to display human-like capabilities such as reasoning, learning, planning and creativity.” Tensions exist between those who are involved in regulating AI, governments and large technology firms, and the design of AI policies to ensure a balance between safe and responsible AI and international competitiveness. In this chapter we present a snapshot of the current AI policies and frameworks from the pivotal actors in AI governance. We also discuss the role of corporate actors in influencing AI regulation and how global AI policies impact international business and innovation. Of particular importance is to ask, Qui bono? – who benefits? – and whether AI risks entrenching economic inequality and exacerbating structural injustice between societal groups and power imbalances between the Global North and the Global South, potentially amplifying the risks and harms, and reducing the benefits of AI for the latter. We argue that AI regulation is further complicated by the fact that AI policies not only reflect national priorities but also mirror the deeply engrained cultural values of the society in which they are developed. Finally, we propose several solutions for the responsible development of AI regulation that balances AI’s potential with protecting global societal welfare.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".