Causation for AI Insurance

Clicks: 15
ID: 328738
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1,435 of 1,787 articles by views in Oxford University Press eBooks

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,787 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract This chapter argues that AI does not render English insurance causation doctrine obsolete, but makes its application evidentially and institutionally harder. It examines proximate causes, concurrent causes, remoteness, intervening acts, and burdens of proof across first-party and liability insurance, showing that AI opacity, partial autonomy, long value chains, and human–machine interaction complicate proof, causal characterization, and policy construction. The chapter further analyses transparency, explainability, and XAI as evidential aids rather than doctrinal preconditions, while stressing their limits for legal attribution. Drawing on English law and selected EU developments, including the AI Act, the revised Product Liability Directive, and the withdrawn AI Liability Directive proposal, it concludes that wholesale reform is unnecessary. The preferable response is targeted adaptation through better drafting, documentation, logging, disclosure, governance, and proportionate regulatory intervention so that insurance causation remains coherent, fair, and market workable in AI-intensive disputes.
Reference Key
openalex_W7213408004 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Feng Wang
Are you one of these authors? Claim your profile
Journal Oxford University Press eBooks
Year 2026
DOI
10.1093/law/9780198953586.003.0005
URL
https://doi.org/10.1093/law/9780198953586.003.0005
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.