This page highlights selected writing across ethics, accessibility, technology, and human-centered systems.
Examines the recursive risks of training artificial intelligence systems on AI-generated data, with a focus on bias amplification, degradation of signal, and implications for system reliability and fairness.
Proposes equity as a measurable performance dimension in machine learning systems, arguing for evaluation frameworks that extend beyond accuracy to include fairness and real-world impact.
Additional published work exploring themes of technology, human experience, and systems-level thinking across literary and interdisciplinary platforms.
This page is intended as a concise overview of selected writing and publication work.