Good and fair

These include dignity, autonomy, privacy, data protection, non discrimination, equality, and fairness.

These include dignity, autonomy, privacy, data protection, non discrimination, equality, and fairness.

Algorithmic and machine learning systems evolve through their lifecycle and as such it is important for the systems in place and technologies to be good and fair at the onset, in their data inputs and throughout the life cycle of use.

The definitions of good and fair are intentionally broad to allow designers and developers to consider all of the users both directly and indirectly impacted by the deployment of an automated decision making system.

Algorithmic systems should also be regularly peer reviewed or audited to ensure that unwanted biases have not inadvertently crept in over time.

Issues around bias may not be evident when AI systems are initially designed or developed, so it's important to consider this requirement throughout the lifecycle of the system.

This context could include relevant social or discriminatory impacts.