Which of the following is NOT considered when evaluating an AI system?

When evaluating an AI system, the focus tends to be on critical aspects that can directly impact its performance, trustworthiness, and ethical implications. The presence of embedded bias, potential for false positives, and system reliability are all crucial factors to assess because they relate to how the AI system will function in practice and how it might affect users or society.

Embedded bias is particularly important because it can influence the fairness of the AI’s outputs, potentially leading to unequal treatment of different groups of individuals. The potential for false positives matters significantly, especially in contexts like healthcare or criminal justice, where incorrect flags can have serious consequences.

System reliability is another vital consideration, as it gauges the system's operational consistency and its ability to perform its intended function under expected conditions. This aspect ensures that the AI behaves in a predictable manner over time.

In contrast, the number of developers involved is not typically a relevant factor when evaluating an AI system's effectiveness or ethical standing. While having more developers can facilitate a broader range of input and potentially better designs, it does not directly impact how the AI will function or its performance in real-world applications. Thus, it is not a primary consideration in the evaluation of an AI system.