20 Key AI Terms Every Lawyer Should Know (With Legal Examples)

AI is rapidly transforming legal practice, from research to litigation strategy. This glossary breaks down 20 essential AI terms every lawyer should know—each paired with legal-specific examples to help you better advise clients, spot risks, and stay current as AI regulations evolve.

20 Key Artificial Intelligence (AI) Terms for Lawyers

1. Artificial Intelligence (AI)

• Explanation: A branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence, such as reasoning, learning, and problem-solving.

• Example: AI systems like IBM Watson assist in legal research by analyzing vast amounts of case law.

2. Machine Learning (ML)

• Explanation: A subset of AI where machines learn patterns from data to improve their performance without explicit programming.

• Example: Predictive coding in eDiscovery uses ML to identify relevant documents in legal cases.

3. Deep Learning

• Explanation: An advanced form of ML that uses neural networks with many layers to analyze complex data.

• Example: Facial recognition software analyzing surveillance footage in criminal cases.

4. Neural Networks

• Explanation: Computational systems inspired by the human brain, used in ML to identify patterns and make decisions.

• Example: Detecting fraudulent transactions in financial crime cases.

5. Natural Language Processing (NLP)

• Explanation: AI technology that enables machines to understand and process human language.

• Example: Legal chatbots providing basic advice or summarizing lengthy contracts.

6. Algorithm

• Explanation: A set of rules or steps a machine follows to solve a problem or complete a task.

• Example: Algorithms used in sentencing tools to assess the risk of recidivism.

7. Training Data

• Explanation: The dataset used to teach a machine learning model to recognize patterns and make predictions.

• Example: Historical court decisions used to train an AI system for predicting case outcomes.

8. Bias

• Explanation: Systematic errors in AI models due to biases in the training data or design.

• Example: AI used in hiring that inadvertently discriminates based on gender due to biased historical data.

9. Black Box Model

• Explanation: An AI system whose decision-making process is not transparent or easily understood.

• Example: Credit scoring algorithms that assign ratings without revealing their methodology.

10. Explainability

• Explanation: The ability to understand and explain how an AI model makes decisions.

• Example: A judge requiring transparency in an AI risk assessment tool used during sentencing.

11. Autonomous Systems

• Explanation: AI-powered machines capable of making decisions and performing tasks independently

• Example: Self-driving cars involved in accidents, raising liability questions.

12. Generative AI

• Explanation: AI systems that create new content, such as text, images, or music, based on input prompts.

• Example: AI-generated contracts or summaries, such as those produced by ChatGPT.

13. Overfitting

• Explanation: When an AI model learns the training data too well and performs poorly on new, unseen data.

• Example: A legal AI tool that works perfectly with one jurisdiction’s data but fails in another.

14. Computer Vision

• Explanation: AI systems that interpret and analyze visual data like images and videos.

• Example: Evidence analysis in video footage for court cases.

15. Ethical AI

• Explanation: The study and practice of ensuring AI systems are developed and used responsibly and fairly.

• Example: Avoiding biased AI tools in hiring or criminal justice.

16. Robotic Process Automation (RPA)

• Explanation: Software robots that automate repetitive, rule-based tasks.

• Example: Automating document review in legal cases.

17. Tokenization

• Explanation: In NLP, breaking text into smaller components, such as words or sentences, for analysis.

• Example: AI analyzing legal briefs for relevant keywords.

18. Reinforcement Learning

• Explanation: A type of ML where machines learn by trial and error to maximize rewards.

• Example: AI optimizing court scheduling to reduce delays.

19. API (Application Programming Interface)

• Explanation: A tool that allows different software systems to communicate and exchange information.

• Example: Integrating an AI-powered legal research tool with a firm’s existing case management software.

20. Data Privacy and Security

• Explanation: Safeguarding sensitive data used by AI systems to ensure confidentiality and compliance with regulations.

• Example: Ensuring GDPR compliance when using AI for cross-border legal matters.