A system of policies, practices, and processes implemented by organizations to manage and oversee their use of AI, ensuring alignment with their goals and ethical, responsible, and legal use.

The extent to which information about an AI system, such as whether it is being used and in what manner, is available to involved parties.

The obligation of creators, operators, and regulators of an AI system to ensure its operation is ethical, fair, transparent, and compliant with standards.

Data provided or acquired directly by an algorithm or learning model to produce a result and serve as the foundation for learning, making predictions, and completing tasks.

A subset of the data used to evaluate the model’s performance during the training phase, used to fine-tune the model’s parameters.

There are various biases in this field that can affect results and jeopardize individual rights and freedoms.

A systematic error or deviation from the true value of a prediction that originates from model assumptions or the data itself.

Leads to systemic prejudice, discriminating against a specific group.

A subfield of AI that helps systems understand, interpret, and manipulate human language, transforming information into content.

A subfield of AI that employs algorithms allowing computer systems to learn iteratively, enabling them to make decisions and draw inferences or predictions based on data.