<p>To overcome the opaqueness of artificial intelligence (AI) operation, post hoc explanation techniques provide clear and interpretable accounts of AI decision-making. While post hoc explanation techniques help alleviate concerns about the “black-box” nature of AI systems, the explanations are not self-evident claims and therefore require justifiable scrutiny. Within the sociological framework, justification is interpreted as compliance with social systems, legal structures, and mechanisms regulating individual behavior. While within the theoretical framework of scientific explanation, it is understood as comprising both evidential and normative components. When using a scientific explanation framework to examine the methods used in post hoc explanations, one finds that the criterion for scientific explanation may be overly strict in evaluating post hoc explanations. Therefore, a shift in the evaluation criteria is needed. From a pragmatic perspective, post hoc explanations could be regarded as offering a form of justification, insofar as they conform to institutional rules and contribute to increased explanatory information. Moreover, in future social interactive scenarios, the credibility and applicability of post hoc explanations are expected to increase.</p>