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Is explainable AI more feasible through symbolic AI or soft computing?

How much each paradigm, symbolic AI and soft computing (or hydrid approaches), adresses explanation and argumentation, where symbolic AI refers e.g. to GOFAI or expert systems and soft computing refers to machine learning or probabilistic methods.

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XAI is relevant to "black box" AI (machine learning methods where the decision making rationale is not apparent, only the structure of the system that led to that decision.)

Symbolic AI, GOFAI, and Expert Systems are both explainable and understood, in that the the decision-making process is designed by humans. (Symbolic AI involves human-readable representations of problems.)

To directly answer, XAI is not only feasible in the latter cases, it is a prerequisite. The difficulty is in making black box decision-making explainable.

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