Artificial Intelligence (AI) plays a crucial role in enhancing the efficiency of Contract Lifecycle Management (CLM) systems by adding the ability to seamlessly track key terms and clauses in a digital contract repository. However, when the functioning of an AI tool is considered, it is indeed designed to operate autonomously, yet it involves training and human collaboration to improve its efficacy and accuracy.
Owing to the needs, high-quality data annotation has emerged as one of the critical processes in AI-powered contract extraction and migration. This is in fact, a fundamental process in training AI for contract analysis because an untrained tool can lead to inaccuracy of data and instances of non-compliance. From a bird’s eye view, AI annotation would appear as mere training through example documents, but this step defines the effectiveness of an AI algorithm for a defined requirement.
Making AI tools smarter with data annotation
Accuracy is the primary goal of AI tools that are built to deal with a vast amount of data (structured or unstructured) in various formats. To help them understand the information, extensive training is inevitable, where data annotation comes into play. It is the process of making an information pattern recognizable for the AI tools and enabling them to deliver intended results.
The efficacy of AI tools for contract analysis is determined by their ability to understand and track key clauses and terms in contracts and other legal documents. Whether it is a non-trained or a semi-trained contract analysis AI tool, data annotation is vital to establish or validate its comprehension of what must be delivered.
What does it take for flawless AI annotation for contract analysis?
A team of highly skilled annotators is primary to enhance the accuracy of data annotation across varied types of contracts. In the case of AI data annotation for contract management systems, a team comprising experienced lawyers, quality assurance specialists, and a project manager can help steer the efforts in the right direction. The reason to involve lawyers in the loop for training legal-specific AI tools is that the accuracy of their results is as optimal as the knowledge of experts involved in the validation process. Since it involves varied types of legal documents and terms, leveraging the expertise of trained lawyers will guarantee precise fine-tuning of an AI tool’s algorithm and, in turn, its comprehension of the subject.
The next step is to set quality standards, i.e., a goal of the annotation and its results. This will help define the process and what is expected of everyone in the team, especially the quality experts who will do intensive scrutiny of each data/clause being annotated for the AI/ML legal contract review training.
Implementing a strong data security protocol is yet another key area of focus as extremely confidential data is being dealt with here. From establishing a secured environment, both physically and virtually, to getting the team’s agreement on the security measures, leave no stones unturned to thwart any possibilities of a data breach.
How Cenza leveraged its human expertise to train an AI/ML tool for contract analysis?
While most legal tech service providers rely on AI technology for the end-to-end process, we at Cenza have been enabling our customers to realize the benefits of lawyer-in-the-loop to achieve maximum value in terms of training AI for improved accuracy. Harnessing our experience in training and fine-tuning AI/ML algorithms, we assisted US-based AI-powered contract management and analytics tool for in-house legal and finance teams. The tool is built to identify and extract metadata automatically and other key terms from contracts, enable deep search and deliver key insights for better tracking and management. A low-margin error is one of the key objectives of building a machine learning model, and human review through manual annotation and tagging is essential for the tool to execute near-flawless contract extraction.
Cenza’s contract review team, which comprises qualified and well-trained lawyers in all stages of contract management, such as drafting, review, negotiation, and summarization, achieved the 99% accuracy threshold. A project spanning multiple layers of review on a diverse set of over 100,000 contracts and annotation of more than 1 million key terms speaks volumes to the ability of our highly skilled review team. This project also exemplifies the need to leverage human expertise in enhancing the efficiency of an AI tool in contract analysis.
If you would like to understand more about our contract data annotation and lawyer-in-the-loop services for legal AI providers, visit our website – Cenza. In addition, talking to our experts can help you gain a better understanding of the services. To connect with our team, please click here.