Can Post-Hoc Explanations Eliminate the Epistemic Opacity
Artificial intelligence systems based on deep learning models are widely applied across various domains,yet their opacity has led to trust issues.Computational scientists are attempting to develop tools to interpret black-box models in order to alleviate these contradictions.By analyzing interpretable technology,this article emphasizes the differentiation between causal explanation and post hoc explanation,along with its significance:Causal explanation requires a complete understanding of the model mechanism,while interpretable technology's elucidation of black boxes does not always concern internal model details,but serves as a remedy when causal explanation is unattainable,still holding heuristic epistemological value.The approximation methods used in post hoc explanation are a vital component of the philosophical study of scientific models,while constructive empiricism also provides support for the understanding of the meaning or value of post hoc explanations in the model mechanism.
opacityexplainable artificial intelligence(XAI)post hoc explanationdispositional causal explanation
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