AI Terms Every Lawyer Should Know
Understanding AI terminology is key to leveraging its potential in your legal practice. Our glossary is specifically designed for lawyers, providing clear, concise definitions of key AI terms to help you navigate the intersection of technology and the law. From foundational concepts to advanced topics, it’s your guide to understanding AI in a legal context.
A
Adversarial Attacks
Attempts to manipulate AI systems by introducing misleading or harmful data to exploit weaknesses in the model.
AI Regulation Sandbox
A controlled environment where organizations can test AI systems and regulatory frameworks without the risk of penalties or breaches.
AI Risk Management
The process of identifying, assessing, and mitigating risks associated with AI technologies, such as bias, data breaches, or system failures.
Algorithm Audit
A systematic evaluation of an AI system's performance, fairness, and compliance with legal and ethical standards.
Algorithmic Fairness
The effort to design AI systems that avoid discriminating against individuals or groups based on factors like race, gender, or age.
API (Application Programming Interface)
A set of rules that allows software applications to communicate with each other, often used to integrate AI tools into workflows.
Artificial Intelligence (AI)
The simulation of human intelligence in machines that are programmed to think, learn, and problem-solve in ways similar to how humans think, learn and problem-solve.
Autonomous Agents
AI systems capable of making decisions and taking actions independently to achieve a specific goal, often seen in complex environments.
B
Bias in AI
The tendency of AI models to reflect or amplify biases present in their training data, potentially leading to unfair or inaccurate outcomes.
Black Box Model
An AI model whose internal workings are not transparent or interpretable, making it difficult to understand how decisions are made.
C
CCPA (California Consumer Privacy Act)
A California law that grants residents rights over their personal data, including the right to know, delete, and opt-out of the sale of their data, imposing obligations on businesses to ensure transparency and compliance.
Cognitive Computing
AI systems designed to mimic human thought processes, including learning, reasoning, and self-correction.
Compliance-by-Design
A proactive approach to embedding regulatory compliance requirements into AI systems during their development.
Computational Law
The use of AI and computational techniques to automate or enhance legal reasoning, analysis, and decision-making.
Concept Drift
A change in the statistical properties of data over time, leading to a decline in AI model performance if not addressed.
D
Data Ethics
The principles governing responsible data collection, storage, and use to ensure fairness, privacy, and respect for individual rights.
Data Governance
The framework of policies, practices, and standards for managing data quality, security, and compliance within an organization.
Data Lake
A centralized repository that stores large volumes of raw data in its original format, enabling flexibility in analysis and usage. Often used to protect confidential information.
Data Minimization
A principle of data governance that requires collecting only the data necessary for a specific purpose to reduce risks of misuse or breaches.
Data Privacy
The legal and ethical management of personal and sensitive data to ensure it is used responsibly and complies with privacy laws like GDPR or CCPA.
Data Provenance
The documentation of the origin, history, and usage of data within a system, ensuring transparency and traceability.
Data Sovereignty
The concept that data is subject to the laws and governance structures of the country in which it is collected or stored.
E
Edge AI
AI systems that process data locally on devices (e.g., smartphones, IoT devices) rather than relying on centralized cloud servers.
Ethical AI
The development and use of AI systems in a manner that is fair, transparent, and accountable, ensuring they do not harm individuals or society.
Explainability
The ability to understand and articulate how an AI model arrived at a specific output or decision.
Explainability Metrics
Quantitative measures used to evaluate how well an AI system's outputs can be interpreted and understood by humans.
Explainable AI (XAI)
AI systems designed with mechanisms that make their outputs interpretable and transparent to users, often for compliance and trust purposes.
F
Federated Learning
A technique that allows AI models to train across multiple decentralized datasets without sharing raw data, enhancing privacy.
Few-shot Learning
A capability of AI models to adapt to new tasks by learning from a few examples.
Fine-tuning
The process of adapting a pre-trained AI model to a specific task or domain by training it further on a smaller, specialized dataset, enhancing its relevance and accuracy.
G
GDPR (General Data Protection Regulation)
A comprehensive EU law that governs data protection and privacy, setting strict rules on how personal data is collected, processed, and stored, with significant penalties for non-compliance.
Generative AI
A type of AI capable of creating new content, such as text, images, or audio, by learning patterns from existing data.
Generative Pre-trained Transformer (GPT)
The architecture underlying models like ChatGPT, which uses unsupervised learning to process and generate human-like text.
H
GDPR (General Data Protection Regulation)
A comprehensive EU law that governs data protection and privacy, setting strict rules on how personal data is collected, processed, and stored, with significant penalties for non-compliance.
Generative AI
A type of AI capable of creating new content, such as text, images, or audio, by learning patterns from existing data.
Generative Pre-trained Transformer (GPT)
The architecture underlying models like ChatGPT, which uses unsupervised learning to process and generate human-like text.
I
Inference (in AI)
The process of applying a trained AI model to new data to generate predictions or outputs.
J
K
Knowledge Distillation
A process where a smaller, simpler AI model is trained to replicate the behavior of a larger, more complex model.
Knowledge Graphs
Data structures that represent information as a network of interconnected entities and relationships, often used to enhance AI understanding.
L
Large Language Model (LLM)
A type of AI model trained on vast amounts of text data to understand and generate human-like language.
M
Model Card
A standardized document that provides information about an AI model's capabilities, limitations, and intended use cases, ensuring transparency and accountability.
Model Drift
The phenomenon where an AI model’s performance degrades over time due to changes in the underlying data or environment.
Multi-modal AI
AI systems capable of processing and integrating data from multiple modalities, such as text, images, and audio, to generate comprehensive outputs.
N
Natural Language Processing (NLP)
A field of AI focused on enabling machines to understand, interpret, and generate human language.
Neural Architecture Search (NAS)
An automated process of designing neural networks, optimizing their structure for better performance.
NIST (National Institute of Standards and Technology)
A U.S. government agency that develops and promotes standards, guidelines, and best practices for technology, including cybersecurity, AI, and data governance, to enhance innovation and protect public interests.
O
Out-of-Distribution (OOD) Data
Data that falls outside the range of what an AI model was trained on, often leading to poor performance or unpredictable results.
Overfitting
A phenomenon where an AI model performs well on its training data but fails to generalize effectively to new data.
P
Pre-trained Model
An AI model that has been trained on a large dataset and can be fine-tuned for specific tasks, reducing the need for extensive data and training.
Privacy-Preserving AI
Techniques that enable AI systems to operate without compromising user privacy, such as differential privacy or homomorphic encryption.
Prompt Bank
A curated collection of pre-designed prompts to improve consistency and accuracy in AI outputs.
Prompt Engineering
The practice of crafting inputs (prompts) to guide AI models like LLMs in producing desired and accurate outputs.
Q
Quantum AI
The application of quantum computing to improve AI algorithms, enabling faster computations and solving complex problems.
R
Red-Teaming AI Models
A practice where a team deliberately tests an AI system's vulnerabilities and weaknesses to identify potential risks.
Reinforcement Learning
A type of machine learning where an AI system learns through trial and error, receiving rewards or penalties based on its actions.
Retrieval-Augmented Generation (RAG)
A technique where an AI model retrieves relevant external data to enhance its ability to generate accurate and informed outputs.
S
Shapley Values (in AI context)
A method used in explainable AI to attribute the contribution of each feature to a model's prediction, providing insights into its decision-making.
Synthetic Data
Artificially generated data created to mimic real-world data while preserving statistical patterns and relationships.
Synthetic Oversampling
A method of creating synthetic examples to balance datasets and reduce bias in AI models.
T
Token
The smallest unit of text an LLM processes. Tokens may represent words, parts of words, or punctuation.
Training Data
The datasets used to train AI models, enabling them to recognize patterns and perform tasks.
Transfer Learning
A machine learning technique where a model trained on one task is repurposed for a different but related task.
Transferable Knowledge
Information learned by an AI system that can be applied to new domains or tasks, improving generalization.
U
V
Versioning (in AI Models)
Keeping track of changes made to AI models, including updates, fine-tuning, or retraining, to ensure accountability and reproducibility.
W
White or Glass Box Model
An AI model with transparent and interpretable internal logic, allowing users to understand its decision-making process.
X
Y
Z
Zero Trust Architecture (ZTA)
A security framework that assumes no implicit trust in any system or user and continuously verifies data access and usage.
Zero-shot Learning
A capability of AI models to perform tasks or answer questions without being explicitly trained on the specific task.
