Election Fraud Data Science

The recent detection of layered election fraud schemes validates the emerging maturity of data science, particularly the difference between hobby level fraud that previously was good enough but now is easily detected by advanced practitioners of data science.

Elections since 2012 have been subject to increasing fraud, particular in voting machines and voting center organization allowing power brokers to “win” elections by using Stalin’s traditional pragmatic focus on controlling the counting of elections rather than the ballot casting part.

Data anomalies jump out at data scientists, announcing either process error in the best case or manipulation in the malignant. Once detected, data scientists are trained to walk back data findings to their origin, which in the case of fraudulent elections reveals the schemes used.

Data science unleashes the next level of information warfare. While plebes feel happy to cast votes and consume political propaganda, their rulers put on the customary show and manipulate vote counting to ensure particular compliant officials are successfully elected to keep the control mechanisms in place. Those who are desirous of fair elections will call for data to be made more open and scrutinized, while those who need to control counting will make data less available and be forced to hire black hat data scientists to find plausible opportunity to change election tabulation data.

Further data science scrutiny on recent election reports will provide better insight into how our democracy operates in practice, which by all indications differs greatly from how the media reports it.

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