Kavita Vemuri

Prateek Sancheti, Kamalakar Karlapalem, Kavita Vemuri

Interlocking directorships-where individuals simultaneously serve on the boards of multiple corporations-can facilitate the exchange of expertise and strategic alignment but also present risks, including conflicts of interest, economic 'oligarchy', and regulatory non-compliance. In contexts such as large, family-controlled corporate conglomerates in India, the manual detection of interlocks is hindered by the high volume of corporate entities and the complex involvement of extended familial networks. This study introduces a scalable, graph-theoretic framework for the systematic identification and analysis of interlocking directorships. Using Breadth-First Search (BFS) traversal, we examined a curated dataset comprising over 50,000 directors, 85,000 companies, and 300,000 director-company affiliations, yielding a comprehensive representation of corporate network structures. Large Language Models (LLMs) were integrated into the analytical pipeline to characterize both personal and professional linkages among directors. Empirical results indicate that 17% of directors hold positions in exactly two companies, while 58.6% maintain directorships in two or more companies. The combined BFS-LLM methodology enables the detection of recurrent director-company clusters, indicative of strong network cohesion, and provides qualitative insights into potential underlying drivers of these interlocks. The proposed approach enhances the capacity for automated, data-driven detection of complex intercorporate relationships, offering actionable implications for corporate governance, regulatory monitoring, and systemic risk assessment.

Ayushi Agrawal, Aditya Kondai, Kavita Vemuri

AI-powered facial assessment tools are reshaping how individuals evaluate appearance and internalize social judgments. This study examines the psychological impact of such tools on self-objectification, self-esteem, and emotional responses, with attention to gender differences. Two samples used distinct versions of a facial analysis tool: one overtly critical (N=75; M=22.9 years), and another more neutral (N=51; M=19.9 years). Participants completed validated self-objectification and self-esteem scales and custom items measuring emotion, digital/physical appearance enhancement (DAE, PAEE), and perceived social emotion (PSE). Results revealed consistent links between high self-objectification, low self-esteem, and increased appearance enhancement behaviors across both versions. Despite softer framing, the newer tool still evoked negative emotional responses (U=1466.5, p=0.013), indicating implicit feedback may reinforce appearance-related insecurities. Gender differences emerged in DAE (p=0.025) and PSE (p<0.001), with females more prone to digital enhancement and less likely to perceive emotional impact in others. These findings reveal how AI tools may unintentionally reinforce and amplify existing social biases and underscore the critical need for responsible AI design and development. Future research will investigate how human ideologies embedded in the training data of such tools shape their evaluative outputs, and how these, in turn, influence user attitudes and decisions.

Bhaiya Vaibhaw Kumar, Deepti Rawat, Tanvi Kandalla et al.

This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homogeneous traffic. Our dataset offers a critical lens into the unique visual attention patterns and insights into the decision-making of Indian two-wheeler drivers. The analysis demonstrates that existing saliency models, like TASED-Net, perform less effectively on the myEye-2Wheeler dataset compared to when applied on the European 4-wheeler eye tracking datasets (DR(Eye)VE), highlighting the need for models specifically tailored to the traffic conditions. By introducing the dataset, we not only fill a significant gap in two-wheeler driver behaviour research in India but also emphasise the critical need for developing context-specific saliency models. The larger aim is to improve road safety for two-wheeler users and lane-planning to support a cost-effective mode of transport.

Nancy Hada, Aditya Singh, Kavita Vemuri

Indian folk paintings have a rich mosaic of symbols, colors, textures, and stories making them an invaluable repository of cultural legacy. The paper presents a novel approach to classifying these paintings into distinct art forms and tagging them with their unique salient features. A custom dataset named FolkTalent, comprising 2279 digital images of paintings across 12 different forms, has been prepared using websites that are direct outlets of Indian folk paintings. Tags covering a wide range of attributes like color, theme, artistic style, and patterns are generated using GPT4, and verified by an expert for each painting. Classification is performed employing the RandomForest ensemble technique on fine-tuned Convolutional Neural Network (CNN) models to classify Indian folk paintings, achieving an accuracy of 91.83%. Tagging is accomplished via the prominent fine-tuned CNN-based backbones with a custom classifier attached to its top to perform multi-label image classification. The generated tags offer a deeper insight into the painting, enabling an enhanced search experience based on theme and visual attributes. The proposed hybrid model sets a new benchmark in folk painting classification and tagging, significantly contributing to cataloging India's folk-art heritage.

Vivek Kaushal, Kavita Vemuri

A corpus of Hindi news headlines shared on Twitter was created by collecting tweets of 5 mainstream Hindi news sources for a period of 4 months. 7 independent annotators were recruited to mark the 20 most retweeted news posts by each of the 5 news sources on its clickbait nature. The clickbait score hence generated was assessed for its correlation with interactions on the platform (retweets, favorites, reader replies), tweet word count, and normalized POS (part-of-speech) tag counts in tweets. A positive correlation was observed between readers' interactions with tweets and tweets' clickbait score. Significant correlations were also observed for POS tag counts and clickbait score. The prevalence of clickbait in mainstream Hindi news media was found to be similar to its prevalence in English news media. We hope that our observations would provide a platform for discussions on clickbait in mainstream Hindi news media.