Natural Language Generation of Explanations of Fuzzy Inference Decisions
Ismaïl Baaj, Jean-Philippe Poli · 2019
As Artificial Intelligence and fuzzy systems are at the center of the emergence of advanced technologies such as autonomous vehicles or medical decision support systems, a problem of trust from a human point of view is strongly appearing. In this article, we tackle the problem of explanation of a fuzzy inference system decision in its entirety: from the conception of an algorithm that produces a textual explanation to its evaluation.We define a function which is able to associate to any activated fuzzy rule, the structure responsible of its activation degree. To assess our method, we defined a protocol to evaluate AIgenerated explanation, and made an experiment: explanations obtained from the classification of pastas. Despite limitations, the results show a good transparency of the reasoning, consistency and good global effectiveness in generated explanations.