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Activating the Annotator: Operationalising Lived Experience in Data Annotation
Sheza Munir, Ramaravind Kommiya Mothilal, Syed Ishtiaque Ahmed
Perspectivist NLP has established that annotator positionality shapes labels for subjective tasks, but the field still lacks methods for operationalising it. Positionality is typic…
Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework
Sheza Munir, Ahanaf Rodoshi, Sumin Lee +3
Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to high-conflict domains, trea…
Dharma, Data and Deception: An LLM-Powered Rhetorical Analysis of Cow-Urine Health Claims on YouTube
Sheza Munir, Ratna Kandala, Anamta Khan +2
Health misinformation remains one of the most pressing challenges on social media, particularly when cultural traditions intersect with scientific-sounding claims. These dynamics a…
When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation
Anamta Khan, Ratna Kandala, Deepti +2
Social media platforms have become primary channels for health information in the Global South. Using gomutra (cow urine) discourse on YouTube in India as a case study, we present…
The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
Sheza Munir, Benjamin Mah, Krisha Kalsi +5
In machine learning, "ground truth" refers to the assumed correct labels used to train and evaluate models. However, the foundational "ground truth" paradigm rests on a positivisti…