AI-driven contract law processes and the efficient breach doctrine: A systematic review of legal challenges in common law jurisdictions
Bibliographic record
Abstract
This study examines the changing role of artificial intelligence (AI) in contract law, focusing on how it interacts with the efficient breach doctrine in common law countries, including the United States, the United Kingdom, Canada, and Australia. A systematic review was conducted, analyzing 187 articles and 3 legal cases from Scopus and Google Scholar. From these, 56 articles and legal cases published over the last five years were selected for detailed analysis. The findings indicate that AI improves efficiency and accuracy in contract management and breach decisions, enhancing legal practice. However, it also raises significant legal and ethical challenges, such as issues of accountability, consent, transparency, and liability. The comparative analysis shows that courts in different countries are adopting AI at different rates, with regulatory frameworks still underdeveloped to address AI-related complexities in contract law. This study offers new insights by identifying areas for legal reform, such as creating new civil law rules, ethical guidelines, standardized documents, and stronger regulatory oversight. By contributing to the discussion on AI's impact on contract law, this research emphasizes the need for future legal frameworks that balance AI's benefits with principles of fairness and justice, promoting both innovation and ethical integrity in AI-based legal processes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".