AI Policy Analyzer Case Study

AI Policy Analyzer on MacBook — MMIP legislative data tool for Urban Indigenous Collective

MMIP data is life data

In 2021, Urban Indigenous Collective initiated a policy tracker to monitor all state-wide and national MMIP-related legislation in the United States. Its goals were concrete: demonstrate the current state of the crisis, track progress toward supporting survivors, and expose which jurisdictions are neglecting the issue entirely.

Behind that mission is a deeper injustice. Indigenous communities face a legacy of data genocide — an intentional erasure through lack of data collection and insufficient funding for new research. This erasure has long suppressed visibility for Indigenous issues, and nowhere is that more damaging than in the MMIP crisis, where absence of data translates directly into absence of policy response.

Addressing that gap wasn't just a technical problem. It was a strategic one: how do you build a system that generates culturally relevant data at scale, fast enough to keep pace with a legislative calendar?

A tangle of conflicting data

The first obstacle was the data itself. UIC's existing MMIP tracker contained conflicting datasets — years of manual entry with no standardized format. Fields like "Sponsors" and "Indigenous Sponsors" had no systematic way to identify which sponsors were Indigenous, leading to underreporting and inaccurate records. Team members had to rely on name recognition or manual research to fill the gap.

Mixing qualitative and quantitative data in the same spreadsheet blocked any kind of automated reporting. Over 25 columns required manual updates, and without a single source of truth, each dataset contradicted the others. The tracker was technically functional — but practically unusable for the kind of rapid, scalable analysis UIC needed.

By leveraging ChatGPT and automating legislative data analysis, we are addressing this data genocide head-on — giving Indigenous communities a way to reclaim and generate culturally relevant data at scale.

Three moves to close the gap

One unified dataset

The first move was merging the conflicting datasets into a single Excel document — reconciling years of inconsistency without losing any critical information. From there, the tracker migrated to AirTable, which provided the standardized data formats, automated visualizations, and interface builder UIC needed to turn raw legislative data into actionable intelligence.

Knowing who is Indigenous

To solve the Indigenous sponsor identification problem, I built a standalone database of Indigenous politicians and legislators — sourced from Wikipedia, the only public data available — and integrated it directly into the policy analyzer. What was once a manual, error-prone research task is now automated: the system cross-references each bill's sponsor list against the database and flags Indigenous legislators without any manual lookup.

This tool exists as a standalone resource as well as an integrated component of the analyzer — available to any researcher who needs to identify Indigenous legislative representation.

ChatGPT as legislative analyst

The analyzer, built in Python, pulls legislative details directly from the Legiscan API and uses GPT-4 to parse bill text through culturally tailored questions — generating new structured data for analysis. What once took over an hour of manual reading and annotation now takes minutes.

The AI-generated analysis is reviewed by volunteers and the MMIP Program Associate before upload, balancing speed with the human oversight that culturally sensitive data demands. Automation handles the volume; people ensure the integrity.

Data reclaimed at scale

The introduction of the AI policy analyzer transformed UIC's data collection and reporting processes. Manual tasks that once took over an hour now take minutes. Conflicting datasets are consolidated. Indigenous sponsors are identified accurately. And the data flowing into AirTable is consistent, structured, and ready for analysis — enabling UIC to push back against the ongoing data genocide in real time.

25+ columns automated Hours → minutes Legiscan API GPT-4 Human-reviewed outputs Standalone + integrated tool

Learnings

A key lesson from this project has been understanding the balance between automation and human oversight when dealing with culturally sensitive data. The tool is designed to amplify human capacity, not replace human judgment — and that distinction matters enormously when the data directly concerns communities facing a public safety crisis.

Clear data governance protocols were critical as the team expanded. As volunteers began contributing to the review process, establishing workflows that enabled participation while ensuring data accuracy and cultural integrity became as important as the technical build itself.

Built with

The analyzer is built in Python, pulling legislative data from Legiscan and running culturally tailored GPT-4 analysis. The Indigenous sponsor identification database draws from Wikipedia. Structured outputs flow into AirTable for visualization and team review.

Python

ChatGPT-4 / OpenAI

AirTable

Legiscan API

Wikipedia (source)

What's next

The Indigenous sponsor identification database remains a standalone tool available for use by any organization researching Indigenous legislative representation. For the policy analyzer itself, the next priority is deeper AirTable integration — automated reporting that generates live metrics from the tracker, reducing the manual effort required to keep the MMIP Policy Tracker current and enabling faster public reporting of legislative trends across Indian Country.