Gaining AI Fluency in the Legal Profession: Understanding Machine Learning
Artificial intelligence (AI) has become a significant influence in our lives and professions, yet its inner workings often remain mysterious to many. This article aims to demystify machine learning, a crucial branch of AI for the legal community, by explaining it in simple terms. By drawing parallels between how machines and humans learn, we can gain insights into the functioning of AI and understand its relevance to our own cognitive processes.
Machine learning is a subset of AI that aims to mimic human learning processes. It involves feeding large amounts of data into algorithms that learn and identify patterns. This enables the algorithms to apply their learnings to new information they encounter. For example, machine learning helps computers differentiate between different agreement types, identify specific clauses within those agreements, and derive meaning from them. Many prominent legal technologies that leverage AI today, including tools like Evisort, Brightflag, and Casetext, use some form of machine learning.
Transfer learning is a technique that leverages knowledge gained from solving one problem and applies it to a different but related problem. It’s like using past experience to tackle new situations. By using pre-trained models that have learned from vast datasets, transfer learning enables more efficient learning on new tasks with less labeled data. This approach accelerates the development of natural language processing (NLP) applications in the legal industry. For example, Google’s search engine uses BERT (Bidirectional Encoder Representations from Transformers), a large language model, to understand the context and meaning of search queries and web pages. LEGAL-BERT, a family of BERT models, is fine-tuned on publicly available legislation, court cases, and contracts, and performs better than BERT out of the box for NLP tasks on law-related information.
Zero-shot learning empowers models to recognize and understand new types of data they have never encountered before. By utilizing additional information or contextual cues about these new types, the models can make accurate predictions. However, it is important to recognize that zero-shot learning has limitations, and human-in-the-loop quality control is essential to ensure reliable results. Factors like the volume of training data, model size, task complexity, and optical character recognition (OCR) scan quality heavily influence zero-shot learning success. As a general rule, zero-shot learning does not work perfectly, so users should rely on human-in-the-loop quality control.
While no model or approach is perfect, understanding the basics of AI and machine learning empowers informed decision-making. When considering AI tools or applications, legal professionals can ask vendors about the machine learning methods employed, the use of pre-trained models, and their fine-tuning process. By gaining AI fluency, legal professionals can harness the power of technology to enhance their work. It enables them to make better decisions about which AI technology to buy and better manage expectations around its effectiveness. By embracing the principles of machine learning and understanding its practical applications, legal professionals can navigate the evolving landscape of AI and leverage its benefits for professional success.
When exploring AI-enabled tools, here are a few questions you can ask technology vendors:
– What machine learning methods are employed in your AI tool?
– Do you use pre-trained models? How are they utilized?
– How do you fine-tune your models for specific legal tasks?
By considering these questions and gaining familiarity with AI technology, legal professionals can maximize the potential of AI to enhance their work and make more informed decisions.
