AI Watermarking

AI Alignment

The research field focused on ensuring AI systems act in accordance with human values, intentions, and ethical principles.

AI Safety

The interdisciplinary field dedicated to making AI systems safe, robust, and beneficial while minimizing risks of unintended consequences.

AI Governance

The frameworks, policies, and regulations that guide the responsible development and deployment of AI technologies.

AI Explainability

The ability to describe AI decision-making processes in human-understandable terms, enabling trust and regulatory compliance.

AI Interpretability

The degree to which humans can understand the internal mechanics and reasoning of an AI model's predictions and decisions.

AI Fairness

The principle of ensuring AI systems make equitable decisions without discriminating against any group based on protected attributes.

AI Transparency

The practice of making AI systems' operations, data usage, and decision processes openly visible to stakeholders.

AI Robustness

The ability of an AI system to maintain performance under varying conditions, adversarial attacks, or noisy input data.

AI Hallucination

When an AI model generates plausible-sounding but factually incorrect or fabricated information with high confidence.

AI Red Teaming

The systematic adversarial testing of AI systems to identify vulnerabilities, failure modes, harmful outputs, and safety risks before deployment.

AI Guardrails

Safety mechanisms and constraints implemented around AI systems to prevent harmful, biased, or policy-violating outputs while preserving useful functionality.

AI Model Card

A documentation framework that provides standardised information about an AI model's intended use, performance characteristics, limitations, and ethical considerations.

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

A metric for evaluating the quality of machine-generated text by comparing it to reference translations or texts.

A structured representation of real-world entities and the relationships between them, used by AI for reasoning and inference.

Artificial intelligence algorithms processed locally on edge devices rather than in centralised cloud data centres.

An approach to AI modelling cognitive processes using artificial neural networks inspired by biological neural structures.

A centralised repository for storing, versioning, and managing trained AI models across an organisation.

The ratio of true positive predictions to all actual positive instances, measuring completeness of positive identification.

A simplified alternative to RLHF that directly optimises language model policies using preference data without requiring a separate reward model.

Embedding

A learned dense vector representation of discrete data (like words or categories) in a continuous vector space.

AI-Generated Content

Text, images, audio, video, and code created by artificial intelligence systems, raising questions about authenticity, intellectual property, and the future of creative work.