Artificial Intelligence Governance can be measured as the following components :
Data : Tracks the data flow from start to end to ensure that the data lineage and provenance is validated to ensure there are no loopholes
Security : If someone in the AI System can manipulate the model’s results by tampering, this can lead to severe issues. You can tackle this in the future by using blockchain to imprint AI Systems.
Cost and value of data : Key performance indicators to track the cost of the data and the value obtained from the algorithm help measure effectiveness continuously.
Bias : Exposing selection and measurement bias with continuous automated tracking can help understand when a model drifts from its initial purpose (through self-learning). You should monitor this constantly to ensure that AI Ethics are maintained.
Accountability : Clarity on the individuals responsible for the system and accountable for its decisions is part of AI Governance of the future. All the way from security loopholes, maintenance, and monitoring
Audit : Audit trails and third party reviews can ensure that systems that affect human life are held accountable.
Time : Model drift and impact over time should be captured to ensure that the model is more efficient than the traditional implementation.
