AI ethics encompasses the principles, frameworks, and practices that guide the responsible development and deployment of AI systems.

AI liability addresses the legal allocation of responsibility when AI systems cause harm—to users, third parties, or property. The question is who bears legal responsibility along the value chain: the foundation model developer, the application developer, the deployer, the operator, or the user.

Computer vision is the field of AI that enables machines to interpret and understand visual information from images and video. It encompasses tasks from basic image classification to complex scene understanding, 3D reconstruction, and visual reasoning.

A neural network is a computational system loosely inspired by biological neurons, organized in interconnected layers of “artificial neurons” (nodes) that process numerical inputs through weighted connections and non-linear activation functions.

Diffusion models are a class of generative AI models that produce data (typically images, but increasingly video and audio) by learning to reverse a gradual noising process.

Synthetic data mirrors a real dataset without copying its records — but generation processes personal data, and the output is anonymous only if it passes re-identification testing. Legal status, sweet spots, and the laundering myth.

A model card is a standardized documentation artifact for an AI model—covering its intended use, training data, performance characteristics, limitations, ethical considerations, and known risks.

Explainable AI (XAI) is the field of techniques and tools that make AI system outputs interpretable to humans—enabling users to understand why a model produced a specific prediction, recommendation, or classification.

Algorithmic bias refers to systematic errors in AI system outputs that produce unfair or discriminatory results, typically disadvantaging protected groups defined by characteristics like race, gender, age, or disability.

AI governance is the set of policies, processes, and oversight mechanisms that organizations and governments use to ensure AI systems are developed and deployed responsibly.