Ensuring the Success of Contract Formation in Agent-Mediated Electronic Commerce
Bibliographic record
Abstract
This article examines a number of contractual issues generated by the advent of intelligent agent applications. The aim of the study is to provide legal guidelines for developers of intelligent agent software by addressing the contractual difficulties associated with automated electronic transactions. The author investigates whether the requirements for a legally enforceable contract are satisfied by agent applications that operate independent of human supervision. Given the relative novelty of the technology and the paucity of case law in the area, the author's observations and conclusions are based on an analysis of first principles in contract law. Additionally, the author provides an analysis of whether proposed and enacted electronic commerce legislation in various jurisdictions is sufficient to cure the inherent deficiencies of traditional contract doctrine. Given the trend towards automated electronic commerce, the author concludes by highlighting the legal requirements that must be met in order to ensure the success of agent technology in the formation of online contracts.
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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 | 0.044 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".