Shion Guha

Scarcity in resource allocation doesn't just amplify inequality—it creates an "Accuracy Trap" that traditional debiasing methods can't escape.

Predictions alone can mislead; understanding how they reshape decision-making is crucial for effective automated systems.

LLMs not only generate biased content but also exhibit second-order bias in their judgments, revealing hidden biases that current safety measures fail to capture.

Institutional transparency in Canada's visa system masks significant disparities in how applicants experience algorithmic decision-making, revealing a troubling gap in accountability.

RAG-enhanced moderation can transform how LLMs address culturally insensitive speech, improving accuracy and community trust.