Unconscionability, Smart Contracts, and Blockchain Technology: are consumers really protected against power abuses in the Digital Economy?
Bibliographic record
Abstract
This work discusses the doctrine of unconscionability in smart contracts involving consumers, as implied by the Supreme Court of Canada in its Uber v Heller decision of 2020. Finding an arbitration clause, whereby Uber required a Toronto driver to bring his labour complaint to an arbitration tribunal in the Netherlands, unenforceable, the Supreme Court required the presence of inequality of bargaining power and improvident bargain for a contract to be unconscionable. This approach to unconscionability also applies to standard form contracts or contracts of adhesion involving consumers. This work focuses on the implications of such an approach for protecting consumers in standard form contracts that take the form of smart contracts. The doctrine of unconscionability may curb abusive practices by companies (particularly global platforms), mitigate the problems associated with the immutability of smart contracts, and incentivize companies to encode fair terms and conditions in smart contracts. This paper, however, raises concerns about the effectiveness of the enforcement of the doctrine of unconscionability in smart contracts implemented within blockchain technologies. Companies’ apparent unwillingness to encode fair terms and conditions in smart contracts and consumers’ inability to detect unfair terms and bring an action in response along with the limitations of regulators and courts to enforce fairness standards in the digital economy may render the doctrine of unconscionability ineffective. Consideration should be given to supplementing regulators and courts with mandatory auditing of smart contracts under public scrutiny, in order to encourage companies to remove unfair terms and conditions together with bias, discrimination, and technical failures. This corporate auditing of smart contracts may greatly mitigate the enforcement problems associated with the doctrine of unconscionability and ensure that consumers are effectively and conveniently protected in the digital economy. Although lessons may be drawn for other nations, attention should also be paid to local contexts and the institutional strengths and weaknesses of a particular country.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".