Use of Artificial Intelligence to Analyse Risk in Legal Documents for a\n Better Decision Support
Bibliographic record
Abstract
Assessing risk for voluminous legal documents such as request for proposal;\ncontracts is tedious and error prone. We have developed "risk-o-meter", a\nframework, based on machine learning and natural language processing to review\nand assess risks of any legal document. Our framework uses Paragraph Vector, an\nunsupervised model to generate vector representation of text. This enables the\nframework to learn contextual relations of legal terms and generate sensible\ncontext aware embedding. The framework then feeds the vector space into a\nsupervised classification algorithm to predict whether a paragraph belongs to a\nper-defined risk category or not. The framework thus extracts risk prone\nparagraphs. This technique efficiently overcomes the limitations of\nkeyword-based search. We have achieved an accuracy of 91% for the risk category\nhaving the largest training dataset. This framework will help organizations\noptimize effort to identify risk from large document base with minimal human\nintervention and thus will help to have risk mitigated sustainable growth. Its\nmachine learning capability makes it scalable to uncover relevant information\nfrom any type of document apart from legal documents, provided the library is\nper-populated and rich.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".