9 citations · 47 across the 16 of their papers we have counts for
16 papers
A Tale of Two Identities: An Ethical Audit of Human and AI-Crafted Personas
Pranav Narayanan Venkit, Jiayi Li, Yingfan Zhou +2
As LLMs (large language models) are increasingly used to generate synthetic personas particularly in data-limited domains such as health, privacy, and HCI, it becomes necessary to…
Can Third-parties Read Our Emotions?
Jiayi Li, Yingfan Zhou, Pranav Narayanan Venkit +4
Natural Language Processing tasks that aim to infer an author's private states, e.g., emotions and opinions, from their written text, typically rely on datasets annotated by third-…
Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach
Sourojit Ghosh, Pranav Narayanan Venkit, Sanjana Gautam +2
Our research investigates the impact of Generative Artificial Intelligence (GAI) models, specifically text-to-image generators (T2Is), on the representation of non-Western cultures…
Race and Privacy in Broadcast Police Communications
Pranav Narayanan Venkit, Christopher Graziul, Miranda Ardith Goodman +2
Radios are essential for the operations of modern police departments, and they function as both a collaborative communication technology and a sociotechnical system. However, littl…
An Audit on the Perspectives and Challenges of Hallucinations in NLP
Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta +5
We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Throu…
From Melting Pots to Misrepresentations: Exploring Harms in Generative AI
Sanjana Gautam, Pranav Narayanan Venkit, Sourojit Ghosh
With the widespread adoption of advanced generative models such as Gemini and GPT, there has been a notable increase in the incorporation of such models into sociotechnical systems…
Automated Detection and Analysis of Data Practices Using A Real-World Corpus
Mukund Srinath, Pranav Venkit, Maria Badillo +3
Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them. In this paper, we propose an automated a…
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis
Pranav Narayanan Venkit, Mukund Srinath, Sanjana Gautam +4
We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets. Our i…
Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lens
Pranav Narayanan Venkit
The rapid growth in the usage and applications of Natural Language Processing (NLP) in various sociotechnical solutions has highlighted the need for a comprehensive understanding o…
Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles
Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar +2
We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Biased NLP models can perpetuate stereotypes and lea…
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias
Vipul Gupta, Pranav Narayanan Venkit, Hugo Laurençon +2
As language models (LMs) become increasingly powerful and widely used, it is important to quantify them for sociodemographic bias with potential for harm. Prior measures of bias ar…
Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models
Pranav Narayanan Venkit, Mukund Srinath, Shomir Wilson
We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framewor…
Sociodemographic Bias in Language Models: A Survey and Forward Path
Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson +1
Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of res…
Nationality Bias in Text Generation
Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar +2
Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP mode…
Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models
Pranav Narayanan Venkit, Shomir Wilson
Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may…
A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source
Pranav Venkit, Zeba Karishma, Chi-Yang Hsu +4
We widely use emojis in social networking to heighten, mitigate or negate the sentiment of the text. Emoji suggestions already exist in many cross-platform applications but an emoj…