Advances in Financial AI: Innovations, Risk, and Responsibility in the Era of LLMs
Bibliographic record
Abstract
The finance sector is seeing a rapid increase in the application of machine learning and AI, with Large Language Models (LLMs), ESG (Environmental, Social, and Governance) investing, and AI Safety significantly reshaping the field. This workshop focuses on how these advancements intersect with core financial AI applications. We will foster interdisciplinary discussion on applying LLMs to finance, addressing challenges in multilingual and non-English markets like Korea. The event will also highlight the integration of ESG signals into algorithmic decision-making and explore AI Safety, emphasizing reliability, fairness, and explainability for AI systems in regulated financial environments. By bringing together experts from academia, industry, and regulatory bodies, the workshop aims to stimulate discussions on practical issues, ethical dilemmas, and cutting-edge research shaping financial AI's future. We welcome submissions that combine technical rigor with societal relevance in AI-driven financial decisions.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".