Friendly Skies, Unfriendly Terms: Class Action Waivers and Force Majeure Clauses in Airline Contracts of Carriage
Bibliographic record
Abstract
The airline contract of carriage. These unassuming bits of language govern the relationship between passengers and their airlines. Over the past three years, a new term has sprouted in these agreements: the class action waiver. Before March 2020, only two of the ten largest United States-based airlines’ contracts of carriage had class action waivers. But as of April 2023, eight now have class action waivers. Why have airlines quickly adopted these copycat terms? What are the implications of this new contractual trend for flyers, airlines, and regulators? This note aims to contribute to the scholarship around these questions in three ways.\nFirst, this note tracks the development of class action waivers and force majeure clauses in airline contracts of carriage between 2020 and 2023. Second, it evaluates the enforceability of existing class action waivers in airline contracts of carriage and outlines possible defenses and challenges, including Airline Deregulation Act pre-emption and unconscionability. Third, it compares the United States’ current system for adjudicating airline-passenger disputes with a Passenger Bill of Rights system, as well as regulatory regimes in Canada, Germany, and the United Kingdom. To conclude, it proposes a system of private alternative dispute resolution modeled after the United Kingdom’s as a possible alternative to the United States’s current litigation-focused system. This solution could help airline defendants avoid classwide liability and its associated costs while ensuring that passengers receive a viable opportunity to obtain redress.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".