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.
They are transparent, auditable, fair, and fully documented.
auditability of AI systems is key in this regard, as the assessment of AI systems by internal and external auditors, and the availability of such evaluation reports, strongly contributes to the trustworthiness of the technology.
External auditability should especially be ensured in applications affecting fundamental rights, including safety critical applications.
Any involvement by an autonomous system in judicial decision making should provide a satisfactory explanation auditable by a competent human authority.
Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise.
To ensure that our use of AI does not inadvertently prejudice the treatment of particular groups in society, we call for the Government to incentivise the development of new approaches to the auditing of datasets used in AI, and to encourage greater diversity in the training and recruitment of AI specialists.
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.
Formulate guidelines and principles on solving bias and discrimination, potential mechanisms include algorithmic transparency, quality review, impact assessment, algorithmic audit, supervision and review, ethical board, etc.
Promote algorithmic transparency and algorithmic audit, to achieve understandable and explainable AI systems
Models, algorithms, data, and decisions should be recorded so that they can be audited in cases where harm is suspected.