In the digital era, legal technology products are becoming a crucial force in the legal services sector. Photo: TUCHONG

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In the digital era, legal technology products are becoming a crucial force in the legal services sector. However, their accuracy faces numerous challenges. Technical benchmarks, user scenarios, and legal logic should be integrated organically to address these challenges.

Technical acceptance benchmarks

Technical acceptance benchmarks serve as the first line of defense to ensure the accuracy of legal technology products.

Accuracy of data extraction: Whether in civil, criminal, or administrative cases, legal technology products must accurately extract key facts such as timelines and legal relationships. This requires high precision in optical character recognition (OCR) and natural language processing (NLP) to eliminate errors.

Effectiveness of analytical algorithms: Products must be able to analyze disputes accurately based on the extracted data. Algorithms should be equipped with profound legal knowledge and logical reasoning capabilities to ensure the credibility and accuracy of analysis results, while also possessing the ability to learn and adapt to evolving legal environments.

Precision in dispute focus matching: Products must accurately match case facts provided by users with cases of the same type within their databases, identifying cases with similar dispute focuses. This necessitates highly precise and stable algorithm design that can provide tailored case recommendations according to the characteristics of different case types.

Real-world user scenarios

The accuracy of legal technology products needs to be verified in real-world scenarios. First, smart case recommendation products should provide robust decision-making support for legal professionals by catering to their needs and preferences. Close collaboration between product development teams and legal experts is necessary to ensure that the analyses align with legal logic and judicial practices. Second, products should be highly customizable and flexible to meet diverse user needs. For example, lawyers may focus on the likelihood of winning a case and the development of litigation strategies, whereas judges may be more concerned with fair trials and the accuracy of legal bases. Third, product accuracy is also affected by case types, geographic locations, and other factors. Contract fulfillment and liability for breach of contract are prioritized in civil and commercial cases, while constituent elements of crime and sentencing circumstances are central to criminal cases. Product design should fully account for the characteristics of different case types as well as the variations across regions and legal systems.

Foundational legal logic

Foundational legal logic bridges technical acceptance benchmarks and real-world user scenarios. First, building foundational models of legal rules is the key to ensuring product accuracy. These models should incorporate comprehensive legal knowledge and logical frameworks to accurately understand and address legal issues across various case types. Second, when setting technical acceptance benchmarks, emphasis should be placed not only on the precision of data extraction and analysis, but also on the accuracy and stability of algorithmic application of legal rules. Third, foundational models can be iteratively optimized by collecting and analyzing user feedback. Fourth, since no two cases are identical in judicial practice, the design of foundational models should accommodate legal rules and judicial practices to ensure reasonable and accurate weight setting.

Organic integration

Taking smart case recommendation products as an example, this article explores how legal technology products can achieve organic integration of technology, user scenarios, and legal logic. First, in the early stages of research and development, it is necessary to devise a series of strict test cases to verify the accuracy of data extraction, analytical algorithms, and dispute focus matching. A certain number of real-world legal cases can be selected for testing. Product stability and security should be comprehensively evaluated as well. Second, it is recommended to invite legal professionals such as lawyers and judges to trial the products to assess their practical effectiveness. Professional feedback can reveal product flaws, guiding subsequent optimization. Third, the development of foundational models should fully consider the complexity of legal rules. These models should have the capability to adapt to evolving legal environments and judicial practices, while taking into account the differences in legal rules across various case types.

Wang Yanling is a professor and director of the Key Laboratory of AI Legal Application in Higher Education Institutions of Guangdong Province.

Edited by WANG YOURAN