Technology, the Changing Nature of Disputes, and the Future of Equitable Principles in Canadian Contract Law
Bibliographic record
Abstract
There are a number of legitimate reasons to be excited about the application of new technologies to make contracting more efficient. Unfortunately, each of those reasons is associated with certain risks for both contractors and contractees. In this article, I argue that an ‘‘equitable” approach to modern contract law — understood by the likes of Larry DiMatteo and others ‘‘not merely as a system of rules, but of rules tempered by standards and principles” — is particularly well suited for counterbalancing some of the undesirable contractual risks introduced by new technologies like blockchain, artificial intelligence, and smart contracts. A historical analysis of Canada’s common law treatment of equity suggests that new technologies — particularly those that encroach on human autonomy in the contracting process — may push decision makers to increasingly draw on equitable analyses in contractual disputes. I propose that Canadian contract law can expect broader and more principled statutory rules and common law tests, the more that human autonomy is removed from the contracting process by technologies like blockchain, artificial intelligence, and smart contracts. Accordingly, I outline four proposals for counterbalancing the undesirable contractual risks that could be introduced by these new technologies: (1) inserting equitable principles into statutes to (a) prohibit the use of unilateral amendment provisions in consumer contracts, (b) prohibit the communication of terms and conditions after contract formation, (c) prohibit the use of waivers to block consumers from bringing class action proceedings, and (d) prohibit self-executory performance clauses in consumer contracts without constructive notice; (2) embracing generalist equitable statutes not limited to special forms of contracts; (3) making legislative reform quicker and more adaptable to technological change; and (4) promoting technical literacy and a deeper, bona fide study of equitable reasoning and principles in law school and professional curricula.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".