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Election forensics: Using machine learning and synthetic data for possible election anomaly detection.

Mali Zhang¹, R Michael Alvarez¹, Ines Levin²

¹Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States of America.

|November 1, 2019

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This study introduces a novel machine learning method to detect potential election fraud. The approach uses synthetic data to identify at-risk polling places and estimate manipulation in democratic elections.

Area of Science:

Political Science
Computer Science
Data Science

Background:

Election integrity is crucial for democratic governance and legitimacy.
Quantitative methods for assessing election integrity have been limited, relying primarily on in-person observation.
Developing objective, data-driven approaches is essential for robust election monitoring.

Purpose of the Study:

To present a novel machine learning methodology for identifying polling places at risk of election fraud.
To develop a quantitative approach for estimating the extent of potential electoral manipulation.
To provide a scalable and objective tool for enhancing election integrity.

Main Methods:

Development of a machine learning model trained on synthetic data.

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Application of the methodology to mesa-level data from national elections.

Utilizing quantitative analysis to identify patterns indicative of electoral manipulation.

Main Results:

The machine learning methodology successfully identified polling places with a higher risk of fraud.
The study provided estimates for the extent of potential electoral manipulation.
The approach demonstrated the feasibility of using synthetic data for training election integrity models.

Conclusions:

Machine learning offers a powerful quantitative tool for assessing election integrity.
The developed methodology can aid in detecting and quantifying electoral fraud.
This approach enhances the ability to assure the legitimacy of democratic elections.