Bibliographic record
Abstract
The use of artificial intelligence (AI) in finance poses a new realm of questions about how it should be regulated, how its effects and consequences are governed and whether it should be subject to a defined code of ethics. Although the full potential of AI in this sector is still in its infancy, Japanese regulators appear to be more advanced in their approach to some of the ethical issues than their counterparts in many parts of the world. This paper asks: how can other areas of the globe learn from the experience of AI in financial services in Asia Pacific and Southeast Asia? And what issues are likely to need attention and action in the near term? To navigate the ethical and operational challenges of AI in financial services, all stakeholders — banking professionals, regulators and technology providers — must prioritise robust data governance, transparency and ethical AI practices. This involves leveraging strategic frameworks like the Generative AI Decision Tree to guide decision making, fostering cross-industry collaboration to establish comprehensive standards and adopting principle-based approaches that balance innovation with accountability. By aligning efforts to promote trust, inclusivity and sustainability, the financial sector can harness the full potential of AI while safeguarding its integrity and resilience.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".