Discrimination: According to the first edition of the EU-U.S. terminology and taxonomy for artificial intelligence, the term “Discrimination” means:
Unequal treatment of a person based on belonging to a category rather than on individual merit. Discrimination can be a result of societal, institutional, and implicitly held individual biases or attitudes that get captured in processes across the AI lifecycle, including by AI actors and organisations, or represented in the data underlying AI systems. Discrimination biases can also emerge due to technical limitations in hardware or software, or the use of an AI system that, due to its context of application, does not treat all groups equally. Discriminatory biases can also emerge in the very context in which the AI system is used. As many forms of biases are systemic and implicit, they are not easily controlled or mitigated and require specific governance and other similar approaches.
