Linking Artificial Intelligence Principles
This may include, but not limited to: making the system as fair as possible, reducing possible discrimination and biases, improving its transparency, explainability and predictability, and making the system more traceable, auditable and accountable.
The traceability of AI systems should be ensured; it is important to log and document both the decisions made by the systems, as well as the entire process (including a description of data gathering and labelling, and a description of the algorithm used) that yielded the decisions.
In large enough data sets these will be diluted since correct actions usually overrun the errors, yet a trace of thereof remains in the data.
Governance frameworks, including standards and regulatory bodies, should be established to oversee processes assuring that the use of A IS does not infringe upon human rights, freedoms, dignity, and privacy, and of traceability to contribute to the building of public trust in A IS.
The transparency, interpretability, reliability, and controllability of AI systems should be improved continuously to make the systems more traceable, trustworthy, and easier to audit and monitor.
Transparent regulation: The development of artificial intelligence should avoid the security risks caused by the technology black box, and it is necessary to ensure the unity of target functions and technologies through the establishment of reviewable, traceable, reputable regulatory mechanisms.
Artificial intelligence should be auditable and traceable.
Transparency is the ability to trace cause and effect in the decision making pathways of algorithms and, in hybrid intelligence systems, of their operators.
AI algorithms must be traceable and transparent and there should be no algorithm discrimination;