zrp 0.1.13

The Zest Race Predictor tool predicts race/ethnicity using a name and address as inputs.

Zest Race Predictor

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Zest Race Predictor (ZRP) allows its users to estimate race and ethnicty based on an individual's name and home address. ZRP improves upon the Bayesian Improved Surname Geocoding (BISG) method developed by RAND Corporation that is used in fair lending analysis at institutions today. Our intent is to improve the accuracy of fair lending analysis by using more data and better math, and thereby improve our understanding of disparate impact and disparate treatment of protected-status borrowers. Armed with a better understanding of the disparities that exist in our financial system, we can highlight inequities create a roadmap to improve equity in access to finance.

ZRP leverages modern predictive algorithms (in particular gradient boosting) in combination with block group and tract-level American Community Survey (ACS) data. By locating each address within a block group or census tract, and referencing demographic attributes associated with that granular geolocation, we can provide trained models with data as high fidelity as Census block group. The predictive features used in the model include such demographic attributes as percentage of residences identifying as a particular race/ethnicity and average houshold income. Example usage and model training procedures are provided.

The models included in this package were trained on publicly-available voter registration data and validated multiple times: on hold out sets of voter registration data and on a national sample of PPP loan forgiveness data. The results were consistent across tests: 20-30% more African Americans correctily identified as African American, and 60% fewer whites identified as people of color as compared with the status quo BISG method.

Performance on the national PPP loan forgiveness dataset was as follows (comparing ZRP softmax with the BISG-80 method):

African American

====================== =========== =========== =========== Statistic BISG ZRP Pct. Diff

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True Positive Rate 0.571 0.700 +23% (F)

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True Negative Rate 0.954 0.961 +01% (F)

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False Positive Rate 0.046 0.039 -15% (F)

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False Negative Rate 0.429 0.300 -30% (F) ====================== =========== =========== ===========

Asian American and Pacific Islander

====================== =========== =========== =========== Statistic BISG ZRP Pct. Diff

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True Positive Rate 0.683 0.777 +14% (F)

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True Negative Rate 0.982 0.977 -01% (U)

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False Positive Rate 0.018 0.023 -28% (F)

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False Negative Rate 0.317 0.223 -30% (F) ====================== =========== =========== ===========

Non-White Hispanic

====================== =========== =========== =========== Statistic BISG ZRP Pct. Diff

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True Positive Rate 0.599 0.711 +19% (F)

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True Negative Rate 0.979 0.973 -01% (U)

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False Positive Rate 0.021 0.027 -29% (F)

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False Negative Rate 0.401 0.289 -28% (F) ====================== =========== =========== ===========

White, Non-Hispanic

====================== =========== =========== =========== Statistic BISG ZRP Pct. Diff

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True Positive Rate 0.758 0.906 +19% (F)

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True Negative Rate 0.758 0.741 -02% (U)

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False Positive Rate 0.242 0.259 +07% (U)

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False Negative Rate 0.241 0.094 -61% (F) ====================== =========== =========== ===========

Notes

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This is the preliminary version and implementation of the ZRP tool. We're dedicated to continue improving both the algorithm and documentation and hope that government agencies, lenders, citizen data scientists and other interested parties will help us improve the model.

Install

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Install requires an internet connection. We recommend installing zrp inside a python virtual environment <https://docs.python.org/3/library/venv.html#creating-virtual-environments>_. The package has been tested on 3.7.4, but should likely work with 3.7.X. ::

pip install zrp

After installing via pip, you need to download the lookup tables using the following command: ::

python -m zrp download

Note: Due to the size and number of lookup tables necesary for the zrp package, total installation requires 3 GB of available space.

Data

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Training Data

The models available in this package were trained on voter registration data from the states of Florida and North Carolina. Summary statistics on these datasets and additional datasets used as validation can be found here <./dataset_statistics.txt>_ .

Consult the following to download state voter registration data:

• North Carolina <https://www.ncsbe.gov/results-data/voter-registration-data>_
• Florida <https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UBIG3F>_
• Alabama <https://www.alabamainteractive.org/sos/voter/voterWelcome.action>_
• South Carolina <https://www.scvotes.gov/sale-voter-registration-lists>_
• Georgia <https://sos.ga.gov/index.php/elections/order_voter_registration_lists_and_files>_
• Louisiana <https://www.sos.la.gov/ElectionsAndVoting/BecomeACandidate/PurchaseVoterLists/Pages/default.aspx>_

American Community Survey (ACS) Data:

The US Census Bureau details that, "the American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. The 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. The 5-year estimates are available for all geographies down to the block group level." ( Bureau, US Census. “American Community Survey 5-Year Data (2009-2019).” Census.gov, 8 Dec. 2021, https://www.census.gov/data/developers/data-sets/acs-5year.html. )

ACS data is available in 1 or 5 year spans. The 5yr ACS data is the most comprehensive & is available at more granular levels than 1yr data. It is thus used in this work.

The Models and API

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The ZRP can be broken down into four main segments: preprocessing, geocoding, American Community Survey(ACS) integration, and modeling/predictions.

Usage and Examples

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To get started using the ZRP, first ensure the download is complete (as described above) and xgboost == 1.0.2

Next, check out the guide in the examples folder.

Authors

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• Kasey Matthews <https://www.linkedin.com/in/kasey-matthews-datadriven/>_ (Zest AI Lead)
• Austin Li <https://www.linkedin.com/in/austinwli/>_ (Harvard T4SG)
• Christien Williams <https://www.linkedin.com/in/christienwilliams/>_ (Schmidt Futures)
• Sean Kamkar <https://www.linkedin.com/in/sean-kamkar/>_ (Zest AI)
• Jay Budzik <https://www.linkedin.com/in/jaybudzik/>_ (Zest AI)

Contributing

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Contributions are encouraged! For small bug fixes and minor improvements, feel free to just open a PR. For larger changes, please open an issue first so that other contributors can discuss your plan, avoid duplicated work, and ensure it aligns with the goals of the project. Be sure to also follow the Code of Conduct <./CODE_OF_CONDUCT.md>_. Thanks!

Releases

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Follow the steps in the releasing doc <./releasing.rst>_ to push new releases to Pypi and Github releases. With respect to Github releases, we provide new releases to ensure relevant data and look up tables requisite for package download and use are consistently up to date. With each release, the version field in the about.py file must be updated correspondingly (instructions in the resleasing doc).

Wishlist

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Support for the following capabilities is planned:

• ...nothing right now! (Got an idea? Submit an issue/PR!)

License

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The package is released under the Apache-2.0 License <https://opensource.org/licenses/Apache-2.0>__.

Results and Feedback

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Generate interesting results with the tool and want to share it or other interesting feedback? Get in touch via [e-mail].