Human oversight may depend on the risks associated with an AI system but should always be meaningful and should thus include effective, transparent monitoring of human values and moral considerations.

3 Ensure transparency, explainability and intelligibility

Two broad approaches to ensuring intelligibility are improving the transparency and explainability of AI technology.

transparency requires that sufficient information (described below) be published or documented before the design and deployment of an AI technology.

transparency will improve system quality and protect patient and public health safety.

For instance, system evaluators require transparency in order to identify errors, and government regulators rely on transparency to conduct proper, effective oversight.

For instance, system evaluators require transparency in order to identify errors, and government regulators rely on transparency to conduct proper, effective oversight.

transparency should include accurate information about the assumptions and limitations of the technology, operating protocols, the properties of the data (including methods of data collection, processing and labelling) and development of the algorithmic model.

Tests and evaluations should be regular, transparent and of sufficient breadth to cover differences in the performance of the algorithm according to race, ethnicity, gender, age and other relevant human characteristics.

Humans require clear, transparent specification of the tasks that systems can perform and the conditions under which they can achieve the desired level of performance; this helps to ensure that health care providers can use an AI technology responsibly.

Responsiveness requires that designers, developers and users continuously, systematically and transparently examine an AI technology to determine whether it is responding adequately, appropriately and according to communicated expectations and requirements in the context in which it is used.