AI Vocabulary for Legal Professionals
The intersection of artificial intelligence (AI) and law is becoming more and more important as technologies evolve and legal professionals learn how to benefit from them! Therefore, it is crucial to equip yourself with the knowledge to tackle AI effectively. As companies increasingly rely on AI and new technologies to enhance their legal operations the first important step is to understand the terminology around AI. Here's a cheat sheet of some of the most used AI vocabulary you will come across.
1. Artificial Intelligence (AI)
Definition: Artificial intelligence, or AI, is a technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.
Example: Software that automatically reviews contracts and highlights sections that need attention.
2. Machine Learning (ML)
Definition: A subset of AI that allows systems to learn from a set of data without being explicitly programmed.
Example: Systems that can predict the likelihood of winning a case based on past case data.
3. Generative AI (GenAI)
Definition: AI systems that create new content based on patterns learned from data.
Example: Tools that help draft legal documents by filling in details based on a set template.
4. Natural Language Processing (NLP)
Definition: AI focused on understanding and generating human language.
Example: Software that helps find relevant cases by understanding questions in plain English.
5. Large Language Models (LLMs)
Definition: AI models trained on extensive text data, capable of generating text that sounds human.
Example: Systems that can draft emails or memos based on bullet points or short descriptions.
6. AI Ethics
Definition: Principles guiding the ethical development and use of AI.
Example: Ensuring that AI tools in the firm do not produce biased recommendations.
7. Responsible AI
Definition: Ethical, transparent AI development and deployment.
Example: Creating rules and policies for how AI tools should be used within the firm.
8. AI Governance
Definition: Policies for responsible AI use within an organization.
Example: A committee that makes sure AI tools are used properly and ethically.
9. AI Bias
Definition: Discriminatory AI behaviours due to biased data or algorithms.
Example: Revising AI tools to ensure they do not unfairly influence decisions based on race or gender.
10. Explainable AI (XAI)
Definition: AI systems that provide clear explanations for their decisions.
Example: Software that not only predicts case outcomes but also explains the reasoning in simple terms.
11. Model Interpretability
Definition: The transparency of an AI model’s decision-making process.
Example: AI that can justify its choice in selecting certain precedents for case strategy.
12. Tokens
Definition: Tokens are the basic units of input and output in a language model - meaning human language transformed into sections, that are understandable by AI models
Example: In natural language processing tasks, tokens typically represent words, subwords, or characters.
13. Embeddings
Definition: Numerical representations of text for semantic understanding.
Example: Systems that recognize synonyms and related terms when searching through legal databases.
14. Transformer Architecture
Definition: A type of AI model particularly suited to processing and extracting information from large amounts of text.
Example: Advanced software that helps summarize long legal documents quickly.
15. Fine-tuning
Definition: Adapting AI models to specific tasks with additional data.
Example: Customizing AI tools to understand and generate documents specific to your legal speciality.
16. Transfer Learning
Definition: Using an AI model trained for one problem to solve related ones.
Example: Adapting a general legal research tool to specialize in trademark law.
17. AI-Assisted Legal Research
Definition: AI tools that enhance legal research efficiency.
Example: Software that quickly finds precedents and related legal arguments.
18. Retrieval-Augmented Generation (RAG)
Definition: Combining information retrieval with generative models for accuracy.
Example: A system that drafts more relevant and precise legal arguments by pulling information from a vast database.
19. Reinforcement Learning (RL)
Definition: AI models learning from rewards or penalties.
Example: Software that improves its suggestions for legal strategies based on feedback from case outcomes.
20. Few-Shot Learning
Definition: AI learning from a few examples.
Example: Software that quickly adapts to draft specific documents after seeing just a few examples.
21. Zero-Shot Learning
Definition: AI performing tasks without any task-specific training.
Example: A new tool that can generate helpful legal advice without prior specific training on your particular type of case.
22. Prompt Engineering
Definition: Designing effective instructions to guide AI models in generating desired outputs.
Example: Crafting clear questions or commands to get the most accurate and relevant information from AI legal research tools.
23. Data Augmentation
Definition: Increasing the available amount of training data to improve AI performance by generating new samples based on existing data.
Example: Expanding a database with varied examples to train AI that can handle diverse legal issues.
24. Federated Learning
Definition: Training AI on decentralized data without sharing the actual data.
Example: Collaborating on AI training with other firms without exposing sensitive information.
25. Synthetic Data
Definition: Artificially created data that mimics real-world data.
Example: Using generated scenarios to train AI in handling complex, unusual legal problems.
This cheat sheet is designed to help startup legal teams dive into AI technologies with confidence. By understanding these concepts, you can leverage AI to streamline your legal operations and go into detail about which new AI solutions might help your daily workflows. Embrace these terms, explore their applications, and drive transformation in your field!
Ready to use AI and ChatGPT now? Here you can find a list of 75+ legal prompts that every legal professional should know:
