Artificial Intelligence and Generalized Qualitative‐Response Models: An Empirical Test on Two Audit Decision‐Making Domains
提出广义定性响应模型，并在两个审计决策问题中与两种经典机器学习模型比较，发现该模型在某些领域可能是有用的替代方案。
ABSTRACT Machine learning methods are currently the object of considerable study by the artificial intelligence community. Research on machine learning carries implications for decision making in that it seeks computational methods that mimic input‐output behaviors found in classes of decision‐making examples. At the same time, research in statistics and econometrics has resulted in the development of qualitative‐response models that can be applied to the same kind of problems addressed by machine‐learning models—particularly those that involve a classification decision. This paper presents the theoretical structure of a generalized qualitative‐response model and compares its performance to two seminal machine‐learning models in two problem domains associated with audit decision making. The results suggest that the generalized qualitative‐response model may be a useful alternative for certain problem domains.