31 citations · 40 across the 4 of their papers we have counts for
7 papers
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
Nishtha Madaan, Adithya Manjunatha, Hrithik Nambiar +4
Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based syste…
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Nishtha Madaan, Inkit Padhi, Naveen Panwar +1
Machine Learning has seen tremendous growth recently, which has led to larger adoption of ML systems for educational assessments, credit risk, healthcare, employment, criminal just…
Fair Transfer of Multiple Style Attributes in Text
Karan Dabas, Nishtha Madan, Vijay Arya +3
To preserve anonymity and obfuscate their identity on online platforms users may morph their text and portray themselves as a different gender or demographic. Similarly, a chatbot…
Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction
Nishtha Madaan, Sameep Mehta, Shravika Mittal +1
The presence of gender stereotypes in many aspects of society is a well-known phenomenon. In this paper, we focus on studying and quantifying such stereotypes and bias in the Man B…
Generating Clues for Gender based Occupation De-biasing in Text
Nishtha Madaan, Gautam Singh, Sameep Mehta +2
Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically.…
Bollywood Movie Corpus for Text, Images and Videos
Nishtha Madaan, Sameep Mehta, Mayank Saxena +3
In past few years, several data-sets have been released for text and images. We present an approach to create the data-set for use in detecting and removing gender bias from text.…