Predicting Employment Notice Period with Machine Learning: Promises and Limitations
Bibliographic record
Abstract
Rapid advances in data analysis techniques—particularly for predictive algorithms—have opened the door for radically new perspectives on legal practice and access to justice. Several firms in North America, Asia, and Europe have set out to use machine-learning techniques to generate legal predictions, raising concerns regarding ethics, reliability and limits on prediction accuracy, and potential impact on case law development. To explore these opportunities and challenges, we consider in depth one of the most litigated issues in Canada: wrongful termination disputes and, more specifically, the question of reasonable notice determination. Beyond the thorough analysis of this question, this paper is also intended to act as a road map for non-technicians (and especially lawyers) on the application of artificial intelligence (AI) methods, illustrating both their potential benefits and limitations in other areas of dispute resolution. To achieve these results, we first created a large dataset by annotating historic cases related to employment termination. This dataset proved useful for assessing the predictability of reasonable notice of termination, that is, the accuracy and precision of AI predictions. In particular, it helped identify the degree of inconsistency in notice period cases, incidentally exposing the limitations of legal predictions. We then developed predictive algorithms to estimate notice periods based on details of the employment period and investigated their accuracy and performance. Moreover, we thoroughly analyzed these algorithms to better understand the judicial process, and in particular to quantify the weight and influence of case-specific features in the determination of reasonable notice. Finally, we closely analyzed cases that were poorly predicted by the algorithms to understand the judicial decision-making process and identify inconsistencies—a strategy that will ultimately yield a deeper practical understanding of case law. This project will open the door to the development of an access-to-justice project and will provide users with an open-access platform for employment legal help ( www.MyOpenCourt.org ).
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".