Explanation-by-Example Based on Item Response Theory

• URL: http://arxiv.org/abs/2210.01638v1
• Date: Tue, 4 Oct 2022 14:36:33 GMT
• Title: Explanation-by-Example Based on Item Response Theory
• Authors: Lucas F. F. Cardoso, Jos\'e de S. Ribeiro, Vitor C. A. Santos, Ra\'issa L. Silva, Marcelle P. Mota, Ricardo B. C. Prud\^encio and Ronnie C. O. Alves
• Abstract summary: This research explores the Item Response Theory (IRT) as a tool to explaining the models and measuring the level of reliability of the Explanation-by-Example approach. From the test set, 83.8% of the errors are from instances in which the IRT points out the model as unreliable.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have characteristics that allow for self-explanation of their predictions. This situation leads researchers in the field and society to the following question: How can I trust the prediction of a model I cannot understand? In this sense, XAI emerges as a field of AI that aims to create techniques capable of explaining the decisions of the classifier to the end-user. As a result, several techniques have emerged, such as Explanation-by-Example, which has a few initiatives consolidated by the community currently working with XAI. This research explores the Item Response Theory (IRT) as a tool to explaining the models and measuring the level of reliability of the Explanation-by-Example approach. To this end, four datasets with different levels of complexity were used, and the Random Forest model was used as a hypothesis test. From the test set, 83.8% of the errors are from instances in which the IRT points out the model as unreliable.

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