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Which observation explains why expert systems remain in use in narrow settings?

  • A.They are cheaper to build than a machine learning system trained on a comparable body of data
  • B.They run on modest hardware
  • C.They can be extended by the end users themselves without any assistance from a knowledge engineer
  • D.Encoded rules are auditable and stable

Answer: D

In regulated settings such as tax, benefits and compliance, the rules are written down already, they change only by legislation, and every decision must be justifiable line by line. That is exactly the shape a rule base fits, and no learned model matches it for auditability. Build cost, hardware needs and user extensibility are not what keeps them alive. The survival of the technology in these niches, long after the general enthusiasm faded, is a good illustration of matching a method to the structure of its problem.

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