Movies reflect society and also hold power to transform opinions. Social biases and stereotypes present in movies can cause extensive damage due to their reach. These biases are not always found to be the need of storyline but can creep in as the author’s bias. Movie production houses would prefer to ascertain that the bias present in a script is the story’s demand. Today, when deep learning models can give human-level accuracy in multiple tasks, having an AI solution to identify the biases present in the script at the writing stage can help them avoid the inconvenience of stalled release, lawsuits, etc. Since AI solutions are data intensive and there exists no domain specific data to address the problem of biases in scripts, we introduce a new dataset of movie scripts that are annotated for identity bias. The dataset contains dialogue turns annotated for (i) bias labels for seven categories, viz., gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. (ii) labels for sensitivity, stereotype, sentiment, emotion, emotion intensity, (iii) all labels annotated with context awareness, (iv) target groups and reason for bias labels and (v) expert-driven group-validation process for high quality annotations. We also report various baseline performances for bias identification and category detection on our dataset.

Quality of requirements written in natural language has always been a critical concern in software engineering. Poorly written requirements lead to ambiguity and false interpretation in different phases of a software delivery project. Further, incomplete requirements lead to partial implementation of the desired system behavior. In this paper, we present a model for harvesting domain (functional or business) knowledge. Subsequently we present natural language processing and ontology based techniques for leveraging the model to analyze requirements quality and for requirements comprehension. The prototype also provides an advisory to business analysts so that the requirements can be aligned to the expected domain standard. The prototype developed is currently being used in practice, and the initial results are very encouraging.