Welcome!
So excited that you’re interested in the policy comp!!! :)
Please fill out the interest form!
About Us
About the Competition
AI Policy
About the Competition
Important Dates:
March 1 - Proposal submissions due
March 15 - Finalists notified of selection
March 20 - Deadline for selected finalists to confirm attendance
April 9 - Deadline for submitting powerpoints for presentations
April 18 - BISF Undergraduate Public Policy Competition
About the Competition
Participants can choose from one of five prompts:
About the Competition
Data Misuse in AI Systems
In recent years, Artificial Intelligence (AI) has reshaped how personal data is collected, analyzed, and reused, heightening long-standing privacy risks. In 2024 alone, AI-related privacy and security incidents rose by 56.4%, with 233 reported cases. As large language models and generative systems expand in capability, they can memorize or reveal personal information, facilitate targeted spear-phishing and voice-cloning scams, and repurpose user data such as resumes, photographs, or location histories for training without consent. These developments compound existing harms, including biased hiring tools and facial-recognition systems linked to false arrests, while unclear collection practices leave individuals with little control over how their information is used. Given these concerns, propose a U.S. policy initiative that regulates the use of personal data in AI systems, establishes legislative oversight across the training-data supply chain, and strengthens requirements for consent.
About the Competition
Environmental Responsibility in AI Development
From predictive models that forecast extreme weather to satellite systems that monitor deforestation and emissions, Artificial Intelligence (AI) is becoming a critical tool in global efforts towards climate adaptation and mitigation. Yet, the massive computing power required to train a single large AI model produces over 626,000 pounds of CO2 equivalent. As a result, the carbon footprint involved in the creation and maintenance of these models risks intensifying the very problems they seek to solve. Moreover, building these technologies often necessitates minerals like cobalt, lithium and tantalum. Extracting these resources comes at a significant environmental cost and can harm nearby communities–especially in low-income countries that have the greatest need for AI-driven climate solutions but the least access to them. Given these concerns, propose a U.S. policy initiative that regulates the environmental impact of AI development and encourages sustainable sourcing of critical minerals to reduce ecological harm, protect vulnerable communities, and promote responsible innovation in climate-focused AI technologies.
About the Competition
AI-Driven Job Displacement
In recent years, Artificial Intelligence (AI) has accelerated the rate of job displacement across the U.S. Between 2023 and 2024 alone, major technology companies including Google, Microsoft, and Duolingo announced thousands of layoffs explicitly tied to AI implementation, while companies like UPS and IBM have publicly committed to reducing or freezing hiring in roles being automated by AI systems. AI is rapidly outperforming human workers in industries such as data analysis, customer support, education, media, and tech as it can streamline scheduling, data processing, and quality control. Displaced workers often face a sudden loss in income, gaps in employer-based healthcare, limited access to retraining opportunities, and prolonged difficulty finding a job in the same field. Thus, considering that over 800 million jobs could be affected by 2030, propose a U.S. policy initiative at mitigating AI-driven job displacement that expands unemployment support, strengthens social safety nets, and invests in workforce transition programs.
About the Competition
Underrepresentation in Biomedical Data
Artificial intelligence (AI) is increasingly used in healthcare to support diagnostics, treatment planning, and public health decision-making. However, many AI algorithms are trained on datasets that underrepresent historically marginalized populations, creating foundational biases that affect model performance across diverse patient groups. For example, convolutional neural networks trained on predominantly white patient skin images have been shown to misclassify lesions in Black patients, contributing to disparities in melanoma outcomes. These challenges are compounded by inconsistent data standards, incomplete metadata, and limited interoperability across health systems. Given these limitations, propose a U.S. policy initiative that establishes national standards for inclusive health data, improves metadata collection, and ensures that AI algorithms are trained and evaluated on these representative datasets.
About the Competition
Choose your own topic. Either:
What if I have never written a policy proposal or done anything with policy?
Components of a Good Policy Proposal
(Also in the Rulebook on the BISF website, but we’ll quickly go over it today)
At minimum you should have:
What You’ll Be Evaluated On
Paper Formatting
Paper Formatting
Resources Available to You
Office Hours
Prizes!
Bragging rights ofc
Top 3 finalists (1st, 2nd, 3rd place) will have their papers published in The Rice Journal of Public Policy
1st Place: $1,000
2nd Place: $750
3rd Place: $500
Any questions?
Fill out this interest form if you would like to be updated on news/info for the competition:
Thank you for coming, and good luck on your proposals!
Feel free to email us if you want to contact us after this info session! We’ll also be hosting related events and office hours in the future!