Detecting Contradictions in Bilingual Legislation: A Systematic Review and Conceptual Architecture

Ensuring legislative consistency is increasingly difficult as legal systems expand and change more frequently, and the problem is sharper where two language versions are equally binding. This systematic review examines artificial intelligence applied to legal and legislative texts, following the PRISMA 2020 guidelines. Searches of Scopus and the Web of Science Core Collection for English-language peer-reviewed journal articles (January 2019–May 2026) returned 497 records, of which 231 studies met the inclusion criteria; 102 supplied the core analytical evidence and 129 characterized the wider research landscape. Publication output rose sharply after 2024 without a matching improvement in reporting and methodological completeness. Within the reviewed corpus, legal summarization and judgment prediction are the most established directions, whereas natural language inference, legal knowledge representation and compliance verification remain underdeveloped. Only four studies released code or datasets, and although thirteen addressed multilingual legal texts, none of them examined bilingual legislative corpora in which both versions carry equal legal force. Work in the reviewed corpus therefore advances individual legal natural language processing (NLP) tasks but offers no integrated framework for detecting contradictions in bilingual legislation. We propose a conceptual four-layer architecture combining legislative text normalization, knowledge-graph representation, hybrid semantic reasoning and explainable human oversight, and identify research priorities for reliable and transparent AI-assisted legislative analysis.