MIT EECS | CS+HASS Undergraduate Research and Innovation Scholar
Risks in the AI Deployment Supply Chain
Electrical Engineering and Computer Science
The AI industry is of interest to policymakers and dangerously ahead of current policy attempts for three key reasons: its simultaneous similarity to and utter distinctness from the familiar software industry, the speed at which it’ s been adapted, and the naturally evolving supply chain that supports its implementation. Current efforts to produce policy ensuring the safe use of ML-powered tools largely focus on understanding the technology’ s unique properties and risks. But this work, lethargic relative to the AI industry’ s superhuman speed, does not consider how the real-world structures implementing ML-powered tools might interact with their inherent risks. This projects seeks to map and visualize a complex, interdependent AI supply chain, establish some of the unconsidered risks that could arise from the current deployment structure, and experimentally show some of these risks in an industry-like setting.
I sought out SuperUROP to get experience with the defined deliverables of a substantial, long term research project. My interest in safe and explainable ML models sets me up well to consider the regulatory implications of failures in the field, and I’ m looking forward to understanding AI use outside of a research context. The SuperUROP framework allows me to both research what I love and develop my communication skills in a scientific context.