Question

How does increasing AI performance often conflict with the desire for explainability?

1) Increasing AI performance sometimes reduces the transparency of input data used in training, making it more difficult to explain decision-making processes.
2) Increasing AI performance sometimes leads to greater model complexity, making it more difficult to explain decision-making processes.
3) Increasing AI performance sometimes leads to certain evaluation metrics no longer being useful, making it more difficult to explain decision-making processes.
4) Increasing AI performance sometimes removes human-in-the-loop (HITL) methods, making it more difficult to explain decision-making processes.

Answer 1

Increasing AI performance often conflicts with the desire for explainability due to factors like reduced transparency of input data, greater model complexity, obsolete evaluation metrics, and decreased human involvement.

Step-by-step explanation:

Increasing AI performance often conflicts with the desire for explainability due to several reasons:

1. Increasing AI performance sometimes reduces the transparency of input data used in training, making it more difficult to explain decision-making processes.
2. When AI models become more complex and train on large amounts of data, it can be challenging to trace back the specific factors that influenced a certain decision.
3. Increasing AI performance sometimes leads to greater model complexity, which also contributes to the lack of explainability.
4. Highly performant AI models often have intricate architectures and numerous layers, making it harder to interpret the decision-making process.
5. Increasing AI performance sometimes leads to certain evaluation metrics no longer being useful.
6. As AI models become more sophisticated, traditional metrics may no longer capture the true performance of the model.
7. This can hinder the ability to explain the decision-making process.
8. Increasing AI performance sometimes removes human-in-the-loop (HITL) methods.
9. To enhance AI performance, some systems eliminate the involvement of humans in the decision-making loop.
10. However, this makes it more difficult to explain the reasoning behind automated decisions.