PREDICTIVE CODING: ADOPTING AND ADAPTING ARTIFICIAL INTELLIGENCE IN CIVIL LITIGATION
Bibliographic record
Abstract
This paper examines how predictive coding, an artificial intelligence (AI) technology, can effectively and efficiently complement the work of lawyers in the area of electronic discovery document review in civil litigation. It begins with a general overview of AI, and how machine learning can be used to automate the document review process in civil litigation. It then proceeds to a comprehensive overview of predictive coding technology and a discussion of legal issues related to the use of predictive coding technology in civil litigation. The legal issues are whether the use of artificial intelligence technology (as opposed to human intelligence) in document review complies with the rules of the court relating to documentary disclosure; and whether litigation privilege applies to seed sets (or training sets) used in training the predictive coding algorithm. Adopting a comparative law methodology, the paper seeks to address these issues. The paper concludes with a brief consideration of legal professionalism issues arising from the adoption of predictive coding technology in civil litigation in the context of Rule 3.1 of the Model Code of Professional Conduct dealing with competency. The paper argues that successful adoption of AI technology in civil litigation will extend the lawyer’s duty of competence to include knowledge of the relevant legal technology.
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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".