Answers.Body: <p>XAI is relevant to "<a href="https://en.wikipedia.org/wiki/Black_box" rel="nofollow noreferrer">black box</a>" AI (machine learning methods where the decision making rationale is not apparent, only the structure of the system that led to that decision.)  </p>
  
  <p>Symbolic AI, GOFAI, and Expert Systems are both explainable and understood, in that the the decision-making process is designed by humans.  (<a href="https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence" rel="nofollow noreferrer">Symbolic AI</a> involves human-readable representations of problems.)</p>
  
  <p>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. </p>
  
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Answers.CreationDate: 2019-06-19T20:22:35.197Z
Answers.LastActivityDate: 2019-06-19T20:22:35.197Z
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Body: <p>Is explainable AI more feasible through symbolic AI or soft computing?</p>
  <p>How much each paradigm, symbolic AI and soft computing (or hybrid approaches), addresses explanation and argumentation, where symbolic AI refers e.g. to <a href="https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence" rel="nofollow noreferrer">GOFAI</a> or expert systems, and soft computing refers to machine learning or probabilistic methods.</p>
  
Tags: <comparison><statistical-ai><explainable-ai><symbolic-ai>
Title: Is explainable AI more feasible through symbolic AI or soft computing?
UserDisplayName: StalkingAletheia
ThreadText: # Is explainable AI more feasible through symbolic AI or soft computing?
  
  (Asked by: username_0 on 2019-06-19)
  
  Is explainable AI more feasible through symbolic AI or soft computing?
  
  How much each paradigm, symbolic AI and soft computing (or hybrid approaches), addresses explanation and argumentation, where symbolic AI refers e.g. to [GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence) or expert systems, and soft computing refers to machine learning or probabilistic methods.
  
  **Tags:** comparison, statistical-ai, explainable-ai, symbolic-ai
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-06-19. Score: 2 | Accepted answer
  
  XAI is relevant to "[black box](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence) 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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