This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
ApartSprints
AI Policy Hackathon at Johns Hopkins University
670822f88b8fdf04a35a4b76
AI Policy Hackathon at Johns Hopkins University
October 28, 2024
Accepted at the 
670822f88b8fdf04a35a4b76
 research sprint on 

Enviro - A Comprehensive Environmental Solution Using Policy and Technology

This policy proposal introduces a data-driven technical program to ensure that the rapid approval of AI-enabled energy infrastructure projects does not overlook the socioeconomic and environmental impacts on marginalized communities. By integrating comprehensive assessments into the decision-making process, the program aims to safeguard vulnerable populations while meeting the growing energy demands driven by AI and national security. The proposal aligns with the objectives of the National Security Memorandum on AI, enhancing project accountability and ensuring equitable development outcomes. The product (EnviroAI) addresses challenges associated with the rapid development and approval of energy production permits, such as neglecting critical factors about the site location and its potential value. With this program, you can input the longitude, latitude, site radius, and the type of energy to be used. It will evaluate the site, providing a feasibility score out of 100 for the specified energy source. Additionally, it will present insights on four key aspects—Economic, Geological, Demographic, and Environmental—offering detailed information to support informed decision-making about each site. Combining the two, we have a solution that is based in both policy and technology.

By 
Arun Nimmagadda, Sohil Shah, Tushar Gidadhubli, Arnav Patel
🏆 
4th place
3rd place
2nd place
1st place
 by peer review
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This project is private