Nimmi Rangaswamy

Sarthak Mahajan, Nimmi Rangaswamy

In recent years, widespread internet adoption and the growth in userbase of various social media platforms have led to an increase in the proliferation of extreme speech online. While traditional language models have demonstrated proficiency in distinguishing between neutral text and non-neutral text (i.e. extreme speech), categorizing the diverse types of extreme speech presents significant challenges. The task of extreme speech classification is particularly nuanced, as it requires a deep understanding of socio-cultural contexts to accurately interpret the intent of the language used by the speaker. Even human annotators often disagree on the appropriate classification of such content, emphasizing the complex and subjective nature of this task. The use of human moderators also presents a scaling issue, necessitating the need for automated systems for extreme speech classification. The recent launch of ChatGPT has drawn global attention to the potential applications of Large Language Models (LLMs) across a diverse variety of tasks. Trained on vast and diverse corpora, and demonstrating the ability to effectively capture and encode contextual information, LLMs emerge as highly promising tools for tackling this specific task of extreme speech classification. In this paper, we leverage the Indian subset of the extreme speech dataset from Maronikolakis et al. (2022) to develop an effective classification framework using LLMs. We evaluate open-source Llama models against closed-source OpenAI models, finding that while pre-trained LLMs show moderate efficacy, fine-tuning with domain-specific data significantly enhances performance, highlighting their adaptability to linguistic and contextual nuances. Although GPT-based models outperform Llama models in zero-shot settings, the performance gap disappears after fine-tuning.

Tanish Taneja, Arihant Tripathy, Nimmi Rangaswamy

As quick commerce (Q-Commerce) platforms in India redefine urban consumption, the use of deceptive design dark patterns to inflate order values has become a systemic concern. This paper investigates the 'Awareness-Action Gap' among Indian university students, a demographic characterized by high digital fluency yet significant financial constraints. Using a qualitative approach with 16 participants, we explore how temporal pressures and convenience-driven architectures override price sensitivity. Our findings reveal that while students recognize manipulative UI tactics, they frequently succumb to them due to induced cognitive load and the normalization of deceptive marketing as a price of capitalism. We conclude by suggesting value-sensitive design alternatives to align commercial incentives with user autonomy in the Global South.

Mallika Subramanian, Shradha Sehgal, Nimmi Rangaswamy

Intelligent Personal Assistants (IPAs) like Amazon Alexa, Apple Siri, and Google Assistant are increasingly becoming a part of our everyday. As IPAs become ubiquitous and their applications expand, users turn to them for not just routine tasks, but also intelligent conversations. In this study, we measure the emotional intelligence (EI) displayed by IPAs in the English and Hindi languages; to our knowledge, this is a pioneering effort in probing the emotional intelligence of IPAs in Indian languages. We pose utterances that convey the Sadness or Humor emotion and evaluate IPA responses. We build on previous research to propose a quantitative and qualitative evaluation scheme encompassing new criteria from social science perspectives (display of empathy, wit, understanding) and IPA-specific features (voice modulation, search redirects). We find EI displayed by Google Assistant in Hindi is comparable to EI displayed in English, with the assistant employing both voice modulation and emojis in text. However, we do find that IPAs are unable to understand and respond intelligently to all queries, sometimes even offering counter-productive and problematic responses. Our experiment offers evidence and directions to augment the potential for EI in IPAs.