AI Alignment

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 Watermarking

Techniques for embedding imperceptible statistical patterns in AI-generated content to enable reliable detection and provenance tracking of synthetic outputs.

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.

AI systems designed and trained for a specific task or narrow range of tasks, such as image recognition or language translation.

A theoretical level of AI that surpasses human cognitive abilities across all domains, including creativity and social intelligence.

A technique where a language model is given a small number of examples within the prompt to guide its response pattern.

An attention mechanism that selectively computes relationships between a subset of input tokens rather than all pairs, reducing quadratic complexity in transformer models.

The ratio of true positive predictions to all positive predictions, measuring accuracy of positive classifications.

A centralised platform for storing, managing, and serving machine learning features consistently across training and inference.

A semiconductor designed specifically for AI and machine learning computations, optimised for parallel processing and matrix operations.

An extension of the World Wide Web that enables machines to interpret and process web content through standardised semantic metadata.