Perfect is the Enemy of Fair: An Analysis of Election Day Error in Ohio\u27s 2012 General Election Through a Discussion of the Materiality Principle, Compliance Standards, and the Democracy Canon

The continual change in and review of election systems have not overcome the reality that elections systems, including Ohio’s system, could not weather a close or controversial election without delay, litigation, or doubt as to the result. If such a conflict would arise, the actions taken in polling places across the state could be critical in determining a victor within the state and possibly the nation. Ohio, like many states, has responded to this circumstance with an incredibly technical and rule driven approach to election administration. This approach to elections administration is deficient for two primary reasons: (1) it refuses to accept that mistakes happen, and (2) the only mistakes that are subject to scrutiny are those that leave a sufficient paper trail that they could be subject to litigation or post-election scrutiny. This Article presents an analysis of Election Day error in Ohio\u27s 2012 general election through a discussion of the materiality principle, compliance standards, and the Democracy Canon, and suggests that a hybrid approach to election administration is necessary for Ohio’s General Assembly and election administrators at every level to better identify those mistakes and incorporate real-time mistake remedies into Election Day procedures. Ultimately, the human factor of elections should be recognized as an opportunity for better voter understanding and participation rather than a barrier in the pursuit of a perfect Election Day

Perfect is the Enemy of Fair: An Analysis of Election Day Error in Ohio\u27s 2012 General Election Through a Discussion of the Materiality Principle, Compliance Standards, and the Democracy Canon

The continual change in and review of election systems have not overcome the reality that elections systems, including Ohio’s system, could not weather a close or controversial election without delay, litigation, or doubt as to the result. If such a conflict would arise, the actions taken in polling places across the state could be critical in determining a victor within the state and possibly the nation. Ohio, like many states, has responded to this circumstance with an incredibly technical and rule driven approach to election administration. This approach to elections administration is deficient for two primary reasons: (1) it refuses to accept that mistakes happen, and (2) the only mistakes that are subject to scrutiny are those that leave a sufficient paper trail that they could be subject to litigation or post-election scrutiny. This Article presents an analysis of Election Day error in Ohio\u27s 2012 general election through a discussion of the materiality principle, compliance standards, and the Democracy Canon, and suggests that a hybrid approach to election administration is necessary for Ohio’s General Assembly and election administrators at every level to better identify those mistakes and incorporate real-time mistake remedies into Election Day procedures. Ultimately, the human factor of elections should be recognized as an opportunity for better voter understanding and participation rather than a barrier in the pursuit of a perfect Election Day

An imbalance-aware deep neural network for early prediction of preeclampsia.

Preeclampsia (PE) is a hypertensive complication affecting 8-10% of US pregnancies annually. While there is no cure for PE, aspirin may reduce complications for those at high risk for PE. Furthermore, PE disproportionately affects racial minorities, with a higher burden of morbidity and mortality. Previous studies have shown early prediction of PE would allow for prevention. We approached the prediction of PE using a new method based on a cost-sensitive deep neural network (CSDNN) by considering the severe imbalance and sparse nature of the data, as well as racial disparities. We validated our model using large extant rich data sources that represent a diverse cohort of minority populations in the US. These include Texas Public Use Data Files (PUDF), Oklahoma PUDF, and the Magee Obstetric Medical and Infant (MOMI) databases. We identified the most influential clinical and demographic features (predictor variables) relevant to PE for both general populations and smaller racial groups. We also investigated the effectiveness of multiple network architectures using three hyperparameter optimization algorithms: Bayesian optimization, Hyperband, and random search. Our proposed models equipped with focal loss function yield superior and reliable prediction performance compared with the state-of-the-art techniques with an average area under the curve (AUC) of 66.3% and 63.5% for the Texas and Oklahoma PUDF respectively, while the CSDNN model with weighted cross-entropy loss function outperforms with an AUC of 76.5% for the MOMI data. Furthermore, our CSDNN model equipped with focal loss function leads to an AUC of 66.7% for Texas African American and 57.1% for Native American. The best results are obtained with 62.3% AUC with CSDNN with weighted cross-entropy loss function for Oklahoma African American, 58% AUC with DNN and balanced batch for Oklahoma Native American, and 72.4% AUC using either CSDNN with weighted cross-entropy loss function or CSDNN with focal loss with balanced batch method for MOMI African American dataset. Our results provide the first evidence of the predictive power of clinical databases for PE prediction among minority populations

An imbalance-aware deep neural network for early prediction of preeclampsia

Preeclampsia (PE) is a hypertensive complication affecting 8-10% of US pregnancies annually. While there is no cure for PE, aspirin may reduce complications for those at high risk for PE. Furthermore, PE disproportionately affects racial minorities, with a higher burden of morbidity and mortality. Previous studies have shown early prediction of PE would allow for prevention. We approached the prediction of PE using a new method based on a cost-sensitive deep neural network (CSDNN) by considering the severe imbalance and sparse nature of the data, as well as racial disparities. We validated our model using large extant rich data sources that represent a diverse cohort of minority populations in the US. These include Texas Public Use Data Files (PUDF), Oklahoma PUDF, and the Magee Obstetric Medical and Infant (MOMI) databases. We identified the most influential clinical and demographic features (predictor variables) relevant to PE for both general populations and smaller racial groups. We also investigated the effectiveness of multiple network architectures using three hyperparameter optimization algorithms: Bayesian optimization, Hyperband, and random search. Our proposed models equipped with focal loss function yield superior and reliable prediction performance compared with the state-of-the-art techniques with an average area under the curve (AUC) of 66.3% and 63.5% for the Texas and Oklahoma PUDF respectively, while the CSDNN model with weighted cross-entropy loss function outperforms with an AUC of 76.5% for the MOMI data. Furthermore, our CSDNN model equipped with focal loss function leads to an AUC of 66.7% for Texas African American and 57.1% for Native American. The best results are obtained with 62.3% AUC with CSDNN with weighted cross-entropy loss function for Oklahoma African American, 58% AUC with DNN and balanced batch for Oklahoma Native American, and 72.4% AUC using either CSDNN with weighted cross-entropy loss function or CSDNN with focal loss with balanced batch method for MOMI African American dataset. Our results provide the first evidence of the predictive power of clinical databases for PE prediction among minority populations.</jats:p