Testing the effectiveness of saliency-based explainability in NLP using randomized survey-based experiments

As the applications of Natural Language Processing (NLP) in sensitive areas like Political Profiling, Review of Essays in Education, etc. proliferate, there is a great need for increasing transparency in NLP models to build trust with stakeholders and identify biases. A lot of work in Explainable AI has aimed to devise explanation methods that give humans insights into the workings and predictions of NLP models. While these methods distill predictions from complex models like Neural Networks into consumable explanations, how humans understand these explanations is still widely unexplored. Innate human tendencies and biases can handicap the understanding of these explanations in humans, and can also lead to them misjudging models and predictions as a result. We designed a randomized survey-based experiment to understand the effectiveness of saliency-based Post-hoc explainability methods in Natural Language Processing. The result of the experiment showed that humans have a tendency to accept explanations with a less critical view