AI RESEARCH
From Model Explanation to Data Misinterpretation: A Cautionary Analysis of Post Hoc Explainers in Business Research
arXiv CS.LG
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ArXi:2408.16987v2 Announce Type: replace Post hoc explainers such as SHAP and LIME are used widely in business research to interpret complex machine learning models. Although they were designed to explain model predictions, there has been an increasing trend in which the explanations they generate are treated as evidence about underlying data relationships. Based on a systematic review of 181 studies, including 56 published in leading journals, we document that this explanation interpretation is widespread and examine its validity.