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

It is only becoming more vital as in sensitive areas like Political Profiling, Review of Essays in AI gains foothold in making critical - and in some cases, Education, etc. proliferate, there is a great need for increasing fatal - decisions in sensitive areas like Healthcare, Finance, transparency in NLP models to build trust with stakeholders Automated Driving, and such-like [8] [9] [10]. The true potential and identify biases. A lot of work in Explainable AI has aimed to devise explanation methods that give humans insights into of these recent advancements in AI can only be realised the workings and predictions of NLP models. While these if the various stakeholders manage to discern the working of methods distill predictions from complex models like Neural AI models and how their predictions are produced, as that is Networks into consumable explanations, how humans understand necessary to incorporate trust. For example, 83% of people these explanations is still widely unexplored. Innate do not understand automated decision-making systems in the human tendencies and biases can handicap the understanding of these explanations in humans, and can also lead to them criminal justice system, and subsequently, 60% oppose its use misjudging models and predictions as a result. We designed in the domain [11]. But besides securing the buy-in of endusers a randomized survey-based experiment to understand the effectiveness and developers through building trust, AI explainability of saliency-based Post-hoc explainability methods also has the potential of identifying AI inaccuracies prior in Natural Language Processing.