AI Explainability

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 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.

A specialised processor designed to accelerate neural network computations in edge devices and mobile platforms.

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

An AI program that emulates the decision-making ability of a human expert by using a knowledge base and inference rules.

Automated machine learning that automates the end-to-end process of applying machine learning to real-world problems.

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

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

Area Under the ROC Curve, a single metric summarising a classifier's ability to distinguish between classes.

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

Compliance

Adherence to laws, regulations, guidelines, and specifications relevant to an organisation's business.