Visible to the public XAI based model evaluation by applying domain knowledge

TitleXAI based model evaluation by applying domain knowledge
Publication TypeConference Paper
Year of Publication2022
AuthorsSrikanth, K S, Ramesh, T K, Palaniswamy, Suja, Srinivasan, Ranganathan
Conference Name2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)
Date Publishedjul
Keywordsartificial intelligence, CNN, Communications technology, Computational modeling, Decision support systems, Deep Learning, face recognition, Facial features, pubcrawl, resilience, Resiliency, Scalability, Training, xai
AbstractArtificial intelligence(AI) is used in decision support systems which learn and perceive features as a function of the number of layers and the weights computed during training. Due to their inherent black box nature, it is insufficient to consider accuracy, precision and recall as metrices for evaluating a model's performance. Domain knowledge is also essential to identify features that are significant by the model to arrive at its decision. In this paper, we consider a use case of face mask recognition to explain the application and benefits of XAI. Eight models used to solve the face mask recognition problem were selected. GradCAM Explainable AI (XAI) is used to explain the state-of-art models. Models that were selecting incorrect features were eliminated even though, they had a high accuracy. Domain knowledge relevant to face mask recognition viz., facial feature importance is applied to identify the model that picked the most appropriate features to arrive at the decision. We demonstrate that models with high accuracies need not be necessarily select the right features. In applications requiring rapid deployment, this method can act as a deciding factor in shortlisting models with a guarantee that the models are looking at the right features for arriving at the classification. Furthermore, the outcomes of the model can be explained to the user enhancing their confidence on the AI model being deployed in the field.
DOI10.1109/CONECCT55679.2022.9865816
Citation Keysrikanth_xai_2022