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Many businesses around the world are adopting AI with the hope of increasing their top-line and bottom-line numbers. The COVID-19 pandemic has further accelerated the journey. While AI technology promises to bring enormous benefits, the challenges come in similar proportions. In the current form, the requirements for transparency and trust are relatively low for AI systems. On the other hand, there is a lot of regulatory pressure for AI systems to be trustworthy and responsible. Challenges still exist both on the methods and theory side and how explanations are used in practice. The objective of this paper is to analyze Twitter data to extract sentiments and opinions in unstructured text. The authors attempted to use contextual text analytics to categorize the twitter data to understand the positive or negative sentiments and feelings for the AI ethical challenges and highlight the key concerns. Text clustering has also been performed on positive and negative sentiments to understand the key themes behind people's concerns.

How Responsible Is AI? Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling