Linking Artificial Intelligence Principles
Continually test and validate algorithms, so that they do not discriminate against users based on race, gender, nationality, age, religious beliefs, etc.
for creators, including those undertaking the validation and certification of AI, the systems’ processes and input data
Therefore not only should AI be designed with the impact on various vulnerable demographics in mind but the above mentioned demographics should have a place in the design process (rather through testing, validating, or other).
In addition, it must be ensured that the proper division of the data which is being set into training, as well as validation and testing of those sets, is carefully conducted in order to achieve a realistic picture of the performance of the AI system.
Given the non deterministic nature of intelligent systems, a system of constant tests and validations must be established, including the inputs made to the system and its overall behavior.
(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.)
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.
Our AI software undergoes thorough testing under real world scenarios to firmly validate they are fit for purpose and that the product specifications are met.
Test and validation
Ensure AI systems go through rigorous test and validation to achieve reasonable expectations of performance
C. For validation and certification of an AI system, transparency is important because it exposes the system’s processes for scrutiny.
validation and Testing
Institutions should use rigorous methods to validate their models and document those methods and results.