Explanation-by-Example Based on Item Response Theory
This work addresses the need for trustworthy AI explanations for end-users, but it is incremental as it builds on existing Explanation-by-Example approaches.
The paper tackles the problem of trust in black-box machine learning models by proposing a method to measure the reliability of Explanation-by-Example techniques using Item Response Theory, finding that 83.8% of errors occur when the model is flagged as unreliable.
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.