AI Interpretability

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 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.

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

Computing systems that simulate human thought processes using self-learning algorithms, data mining, pattern recognition, and natural language processing.

A technique where a smaller, simpler model is trained to replicate the behaviour of a larger, more complex model.

Reducing the precision of neural network weights and activations from floating-point to lower-bit representations for efficiency.

A measurement of how well a probability model predicts a sample, commonly used to evaluate language model performance.

Machine learning techniques optimised to run on microcontrollers and extremely resource-constrained embedded devices.

The process of determining cause-and-effect relationships from data, going beyond correlation to establish causation.

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