Linking Artificial Intelligence Principles
Regarding system decision making processes, data structures, and the intent of system developers and technological implementers: be capable of accurate description, monitoring, and reproduction; and realize explainability, predictability, traceability, and verifiability (可解释、可预测、可追溯和可验证) for algorithmic logic, system decisions, and action outcomes.
One possibility is to require algorithmic verifiability rather than full algorithmic disclosure.
Algorithmic verifiability would require companies to disclose not the actual code driving the algorithm but information allowing the effect of their algorithms to be independently assessed.
In addition, AI systems should integrate safety and security by design mechanisms to ensure that they are verifiably safe at every step, taking at heart the physical and mental safety of all concerned.
AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.
Human is responsible for continuous checking and verification of evolved AI to keep its harmony with legal human and legal AI.
(The mechanisms by which transparency is provided will vary significantly, for instance 1) for users of care or domestic robots, a why did you do that button which, when pressed, causes the robot to explain the action it just took, 2) for validation or certification agencies, the algorithms underlying the A IS and how they have been verified, and 3) for accident investigators, secure storage of sensor and internal state data, comparable to a flight data recorder or black box.)
Members of the JSAI must verify the performance and resulting impact of AI technologies they have researched and developed.
Developers should pay attention to the verifiability of inputs outputs of AI systems and the explainability of their judgments.
It is desirable that developers pay attention to the verifiability of the inputs and outputs of AI systems as well as the explainability of the judgment of AI systems within a reasonable scope in light of the characteristics of the technologies to be adopted and their use, so as to obtain the understanding and trust of the society including users of AI systems.
In order to assess the risks related to the controllability of AI systems, it is encouraged that developers make efforts to conduct verification and validation in advance.
verification and validation are methods for evaluating and controlling risks in advance.
● To make efforts to conduct verification and validation in advance in order to assess and mitigate the risks related to the safety of the AI systems.
● To make efforts to conduct verification and validation in advance in order to assess and control the risks related to the security of AI systems.
• Risks of failures in verifying the judgment and the decision making of AI (risks of failure to analyze the interactions between AI systems because the interactions become complicated).
AI service providers and business users should pay attention to the verifiability of inputs outputs of AI systems or AI services and the explainability of their judgments.
In order to ensure the verifiability of the input and output of AI, AI service providers and business users may be expected to record and preserve the inputs and outputs.
7) We must at all times be able to verify that AIS are doing what they were programmed for and what they are used for.
AI systems should be verifiably secure and controllable throughout their operational lifetime, to the extent permitted by technology
When we deliver AI based products and services to our customers in collaboration with partners or third parties, we contractually reserve the right to verify with our suppliers that the logic and data used as declared by the suppliers are true.