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Paper Number
2695
Paper Type
short
Description
Advanced AI models are powerful in making accurate predictions for complex problems. However, these models often operate as black boxes. This lack of interpretability poses significant challenges, especially in high-stakes applications such as finance, healthcare, and criminal justice. Explainable AI seeks to address the challenges by developing methods that can provide meaningful explanations for humans to understand. When black box models are used for prediction, they inevitably produce errors. It is important to appropriately explain incorrect predictions. This problem, however, has not been addressed in the literature. In this study, we propose a novel method to provide explanations for misclassified cases made by black box models. The proposed method takes a counterfactual explanation approach. It builds a decision tree to find the best counterfactual examples for explanations. Incorrect predictions are rectified using a trust score measure. We validate the proposed method in an evaluation study using real-world data.
Recommended Citation
Asrzad, Amir and Li, Xiaobai, "Counterfactual Explanations for Incorrect Predictions Made by AI Models" (2023). ICIS 2023 Proceedings. 6.
https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/6
Counterfactual Explanations for Incorrect Predictions Made by AI Models
Advanced AI models are powerful in making accurate predictions for complex problems. However, these models often operate as black boxes. This lack of interpretability poses significant challenges, especially in high-stakes applications such as finance, healthcare, and criminal justice. Explainable AI seeks to address the challenges by developing methods that can provide meaningful explanations for humans to understand. When black box models are used for prediction, they inevitably produce errors. It is important to appropriately explain incorrect predictions. This problem, however, has not been addressed in the literature. In this study, we propose a novel method to provide explanations for misclassified cases made by black box models. The proposed method takes a counterfactual explanation approach. It builds a decision tree to find the best counterfactual examples for explanations. Incorrect predictions are rectified using a trust score measure. We validate the proposed method in an evaluation study using real-world data.
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