If we cannot conceive a method to calculate computational and human biases, then it is impossible to devise an algorithm to compute systemic biases programmatically. We must rely on human judgment to spot the systemic bias in the dataset. Furthermore, it has to be specific to a particular dataset with a distinct AI prediction goal. There are no generalization rules and no fairness matrix to follow.
Systemic biases in AI are the most notorious of all AI biases. Simply put, systemic discrimination is when a business, institution, or government limits access to AI benefits to a group and excludes other underserved groups. It is insidious because it hides behind society’s existing rules and norms. Institutional racism and sexism are the most common examples. Another AI accessibility issue in everyday occurrences is limiting or excluding admission to people with disabilities, such as the sight and hearing impaired.
The poor and the underserved have no representation...