AI Fairness

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 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 harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives.

An initial instruction set provided to a language model that defines its persona, constraints, output format, and behavioural guidelines for a given session or application.

The field of AI dedicated to representing information about the world in a form that computer systems can use for reasoning.

Querying a language model to perform a task it was not explicitly trained on, without providing any examples in the prompt.

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

An inference strategy that starts with a goal and works backward through rules to determine what facts must be true.

The ability of large language models to learn new tasks from examples provided within the input prompt without parameter updates.

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