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Automated consistency analysis for legal contracts

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2025

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Artificial Intelligence and Law. Springer. ISSN 0924-8463. eISSN 1572-8382. Verfügbar unter: doi: 10.1007/s10506-025-09456-8

Zusammenfassung

Business contracts, particularly sale and purchase agreements, often contain a large number of clauses and are correspondingly long and complex. In practice, it is therefore a great challenge to keep track of their legal context and to identify and avoid inconsistencies in such contracts. Against this background, we describe a method and tool called ContractCheck which allows for the consistency analysis of legal contracts, in particular share purchase agreements (SPAs). In order to identify the concepts that are relevant for an analysis we define an ontology for SPAs. The analysis is, then, based on an encoding of the preconditions for the execution of the clauses of an SPA, as well as on a set of proposed consistency constraints formalized using decidable fragments of first-order logic (FOL). Based on the ontology for SPAs, textual SPAs are first encoded in a structured natural language format that we refer to as “blocks”. ContractCheck interprets these blocks and constraints and translates them into assertions formulated in FOL. It then invokes a Satisfiability Modulo Theory (SMT) solver in order to check the executability of a considered contract, either by providing a satisfying model, or by proving the existence of conflicting clauses that prevent the contract from being executed. We illustrate the application of ContractCheck to concrete SPAs, including one example of an SPA of realistic size and complexity, and conclude by suggesting directions for future research.

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ISO 690KHOJA, Alan, Martin KÖLBL, Stefan LEUE, Rüdiger WILHELMI, 2025. Automated consistency analysis for legal contracts. In: Artificial Intelligence and Law. Springer. ISSN 0924-8463. eISSN 1572-8382. Verfügbar unter: doi: 10.1007/s10506-025-09456-8
BibTex
@article{Khoja2025-05-13Autom-73698,
  title={Automated consistency analysis for legal contracts},
  year={2025},
  doi={10.1007/s10506-025-09456-8},
  issn={0924-8463},
  journal={Artificial Intelligence and Law},
  author={Khoja, Alan and Kölbl, Martin and Leue, Stefan and Wilhelmi, Rüdiger}
}
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