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Red teaming in AI governance refers to the process of intentionally testing an AI system’s safeguards by simulating adversarial attacks or challenging scenarios.

Reinforcement Learning from Human Feedback (RLHF) trains a reward model from human input, optimizing agent policy via RL algorithms.

Recall is a measure of algorithm efficacy, Recall measures the proportion of actual positives that were correctly identified.

A set of practices that seek to design, develop, and deploy AI with good intention to empower employees and businesses, and fairly impact customers and society, allowing for accountability and transparency.

Adherence to laws, regulations, guidelines, and specifications relevant to AI and machine learning is called regulatory compliance.

One of the three main machine learning techniques where an algorithm is trained using trial and error and the model is rewarded for more desirable outputs.

Quality of a system to be safe and secure and not vulnerable to tampering with, or compromising, the data it is trained on.