If the previous chapter offered a framework for testing AI, we now examine the sensitive topic of ethics of AI systems. Ethics is a vast and complex field of study that has absorbed many human minds for a very long time and much exceeds the scope of this chapter. The Toronto Declaration, which was released in May 2018, is the first universally adopted reference statement that provides guidelines to preserve human rights in the age of artificial intelligence. AI should not create new barriers to equality, representation, or diversity. When discrimination happens and prevention is not sufficient, a system should be questioned and harmed promptly.
It specifically points out the crucial responsibility of all Governments and private sector players in preventing and mitigating discriminatory risks in the design, development, and application of machine learning. They must also ensure that proper remedies are available before, during, and after system deployment.
- Can the AI system under test demonstrate that it is equitable and free of discrimination?
- What can we do to mitigate risk and improve the fairness of artificial intelligence systems?
In this chapter, we give guidelines to assist quality engineers in testing artificial intelligences (and related systems) fairly, ensuring that they make reasonable, explainable, and intelligible decisions and are not biased in their design, training, or operation. In other words, ethical testing should be used in conjunction with traditional testing. Utilize the sections that make sense for you and the work at hand. Inquire and do not assume.