6 papers
Detecting LLM-Assisted Academic Dishonesty using Keystroke Dynamics
Atharva Mehta, Rajesh Kumar, Aman Singla +3
The rapid adoption of generative AI tools has heightened concerns regarding academic integrity, as students increasingly engage in dishonest practices by copying or paraphrasing AI…
Who Gets Heard? Rethinking Fairness in AI for Music Systems
Atharva Mehta, Shivam Chauhan, Megha Sharma +5
In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and tr…
Women, Infamous, and Exotic Beings: A Comparative Study of Honorific Usages in Wikipedia and LLMs for Bengali and Hindi
Sourabrata Mukherjee, Atharva Mehta, Sougata Saha +2
The obligatory use of third-person honorifics is a distinctive feature of several South Asian languages, encoding nuanced socio-pragmatic cues such as power, age, gender, fame, and…
Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South
Atharva Mehta, Shivam Chauhan, Monojit Choudhury
Recent advances in generative AI have sparked renewed interest and expanded possibilities for music generation. However, the performance and versatility of these systems across mus…
Exploring Adapter Design Tradeoffs for Low Resource Music Generation
Atharva Mehta, Shivam Chauhan, Monojit Choudhury
Fine-tuning large-scale music generation models, such as MusicGen and Mustango, is a computationally expensive process, often requiring updates to billions of parameters and, there…
Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation Models
Atharva Mehta, Shivam Chauhan, Amirbek Djanibekov +3
The advent of Music-Language Models has greatly enhanced the automatic music generation capability of AI systems, but they are also limited in their coverage of the musical genres…