Machine Learning Model Interpretability in NLP and Computer Vision Applications

In light of the recent advancements in Artificial Intelligence (AI), the application of Machine Learning in the domains of Natural Language Processing and Computer Vision is increasing by leaps and bounds. Deployment of Machine Learning Models in applications (apps) have been rampant with the aim of achieving automation, mainly involving textual and image data. Textual data indulges the subject of Text Analytics (also known as NLP) into action and image data indulges the subject of Computer Vision into play. But, the performance of Machine Learning in the domains of Text Analytics or Vision needs to be judged before deployment. Now, performance analysis of ML Models are done with the help of performance metrics, most importantly AUC Score in Classification Problems, but justification by means of numerical scores only, can't establish the relevance of the Model Performance with Domain Knowledge. In this paper, 1 standard NLP use-case and 4 Computer Vision use-cases are considered for ML Model Interpretability Enhancement that can throw light on the relevance with the concerned Domain Knowledge, the use-case deals with.