Dictionary · EU-U.S. Terminology and Taxonomy for Artificial Intelligence - Second Edition

Trustworthy AI has three components: (1) it should be lawful, ensuring compliance with all applicable laws and regulations (2) it should be ethical, demonstrating respect for, and ensure adherence to, ethical principles and values and (3) it should be robust, both from a technical and social perspective, since, even with good intentions, AI systems can cause unintentional harm. Characteristics of Trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. Trustworthy AI concerns not only the trustworthiness of the AI system itself but also comprises the trustworthiness of all processes and actors that are part of the AI system’s life cycle. Trustworthy AI is based on respect for human rights and democratic values.

A system’s level of independence from human involvement and ability to operate without human intervention. [Different AI systems have different levels of autonomy.] An autonomous system has a set of learning, adaptive and analytical capabilities to respond to situations that were not pre-programmed or anticipated (i.e., decision-based responses) prior to system deployment. Autonomous or semi-autonomous AI systems can be characterised as "human-in-the-loop", "human-on-the-loop", or "human-out-of-the loop" systems depending on their level of meaningful involvement of human beings.

AI-generated or manipulated image, audio or video content that resembles existing persons, objects, places or other entities or events and would falsely appear to a person to be authentic or truthful.

An approach to machine learning which addresses problems of data governance and privacy by training algorithms collaboratively without transferring the data to a central location. Each federated device trains on data locally and shares its local model parameters instead of sharing the training data. Different federated learning systems have different topologies that involve different ways of sharing parameters.

A core component of an AI system used to make inferences from inputs in order to produce outputs. A model characterizes an input-to-output transformation intended to perform a core computational task of the AI system (e.g., classifying an image, predicting the next word for a sequence, or selecting a robot's next action given its state and goals).

A branch of Artificial Intelligence (AI) that focuses on the development of systems capable of learning from data to perform a task without being explicitly programmed to perform that task. Learning refers to the process of optimizing model parameters through computational techniques such that the model's behaviour is optimized for the training task.

When one or more features of an AI system, such as processes, the provenance of datasets, functions, output or behaviour are unavailable or incomprehensible to all stakeholders – usually an antonym for transparency.

The field concerned with machines capable of processing, analysing, and generating human language, either spoken, written or signed.

Reinforcement learning (RL) is a subset of machine learning that allows an artificial system (sometimes referred to as an agent) in a given environment to optimize its behaviour. Agents learn from feedback signals received as a result of their actions, such as rewards or punishments, with the aim of maximizing the received reward. Such signals are computed based on a given reward function, which constitutes an abstract representation of the system's goal. The goal could be, for example, to earn a high video game score or to minimize idle worker time in a factory