AI Guardrails

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 Watermarking

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

AI Model Card

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

The process of using a trained AI model to make predictions or decisions on new, unseen data.

Problem-solving techniques that use practical rules of thumb to find satisfactory solutions when exhaustive search is impractical.

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

A statistical approach to AI that uses Bayes' theorem to update probability estimates as new evidence becomes available.

An automated technique for designing optimal neural network architectures using search algorithms.

A theoretical form of AI that would have consciousness, self-awareness, and the ability to truly understand rather than simulate understanding.

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

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