The future of corporate insolvency law: A review of technology and <scp>AI</scp>‐powered changes
Bibliographic record
Abstract
Abstract The conversation about how artificial intelligence (AI) might affect various areas of law (and other areas of life) has, in recent months, centred around ChatGPT which is just one application of AI. This article takes a broader view and assesses how AI, and technology more broadly, has begun to transform, and will continue to transform corporate insolvency law. While the pandemic has increased the adoption of technology in corporate insolvency processes, there is scope for further transformation. This article aims to survey the technological changes to corporate insolvency law and practice thus far and assess, based on current advances in technology, the potential for further transformation. It advances the argument that technology can improve efficiencies both prior to and during formal insolvency resolution processes. It therefore would be in the interests of every country to facilitate the adoption of technology at various points in the insolvency process. The article takes a cross‐jurisdictional approach to identify tech advances in insolvency law across different countries based on which best practices and guidelines can be outlined.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".