AI Model Card

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 Guardrails

Safety mechanisms and constraints implemented around AI systems to prevent harmful, biased, or policy-violating outputs while preserving useful functionality.

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

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

The ability of AI models to perform tasks they were not explicitly trained on, using generalised knowledge and instruction-following capabilities.

Middleware that manages routing, fallback, load balancing, and model selection across multiple AI providers to optimise cost, latency, and output quality.

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

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

Empirical relationships describing how AI model performance improves predictably with increases in model size, training data volume, and computational resources.

A machine learning approach where models are trained across decentralised devices without sharing raw data, preserving privacy.