Probabilistic Reasoning: Visualization Tools

 Explainability problems are not only related to a particular type of machine learning algorithm. For example, probabilistic reasoning or fuzzy logic-based decision support systems might be challenging to manage and troubleshoot. This has been true from the early days of rule-based systems, starting with #MYCIN and similar implementations. Chapter 21 on probability and ambiguity is an excellent treatment of the problem. The graphical procedure for visualizing probabilities shown by the author can be translated to more complex domains than the three-piece stick example. When the number of fuzzy statements grows, a clear geometrical analogy is probably the only way to manage complexity. Martin Gardner provides excellent references that belong to the canon of probabilistic analysis, e.g., the paper on Erroneous Beliefs in Estimating Posterior Probability by S. Ichikawa and H. Takeichi or N. Starr's A paradox in Probability Theory. #complexity #machinelearning #ai #aiethics #fuzzylogic #probability #reasoning #logic