Leveraging AI to reshape the future of courts

AI is a computer science field that develops systems for tasks that typically require human intelligence, such as problem-solving and understanding different languages. AI generates content, predictions, and recommendations for the environments it interacts with. It can be configured to take specific actions, such as classifying e-filing documents and queuing based on document type or court division, among many other uses.

This is the primary method used for computer systems to identify specific data and patterns, and this subset of AI draws from many areas, such as statistics, social science, linguistics, neuroscience, and cognitive science. The purpose is for the machine to acquire skills or knowledge from human experience by synthesizing concepts and historical data. Currently, there are over 14 learning approaches, and many combinations may factor into the continuous learning process of AI software. 

Initial machine learning will be supervised. This means a human, such as a business analyst, will identify and label data so the software can learn. Generally, the software must reach a certain accuracy threshold before it is used in production.

Once the software is in production, it still learns continuously by having documents that are below the accuracy threshold to be reviewed, verified, and labeled by humans. The longer the software learns, the higher the accuracy and consequently, there are fewer items needing review.

Natural Language Processing is an AI subfield that focuses on the interaction between computers and human language. It uses algorithms and techniques to enable computers to understand, interpret, and generate human language in meaningful and useful ways. The goal of NLP is to enable computers to communicate with humans in their natural language, enabling a range of applications such voice assistants, sentiment analyzers, and more.

Large Language Models are trained on billions of pages of text available online to predict the next word in a sequence of words. To do that, LLMs use the memory of seeing an exact or a similar sequence before so it can guess what should come next. This training enables LLMs to do many things, including translating languages, answering questions, engaging in conversations, and recognizing the context of information in court documents.

This system generates content based on learned information. ChatGPT is one example, and here are some areas where this type of AI has been effective:

• Image generation: Creating realistic images that resemble photos or works of art
• Text generation: Creating contextually relevant text, such as news articles, reviews, poetry, or dialogue
• Music composition: Creating original music based on existing musical styles or patterns
• Video synthesis: Generating video sequences or creating or altering scenes and making new video content (such as Deepfake videos)
• Data augmentation: Generating training data for machine learning models to enhance performance