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To support the 2019 U.S. Supreme Court case Flowers v. Mississippi, APM Reports collated historical court records to assess whether the State exhibited a racial bias in striking potential jurors. This analysis used backward stepwise logistic regression to conclude that race was a significant factor, however this method for selecting relevant features is only a heuristic, and additionally cannot consider interactions between features. We apply Optimal Feature Selection to identify the globally-optimal subset of features and affirm that there is significant evidence of racial bias in the strike decisions. We also use Optimal Classification Trees to segment the juror population subgroups with similar characteristics and probability of being struck, and find that three of these subgroups exhibit significant racial disparity in strike rate, pinpointing specific areas of bias in the dataset.
The underlying assumption of many machine learning algorithms is that the training data and test data are drawn from the same distributions. However, the assumption is often violated in real world due to the sample selection bias between the training
In current hate speech datasets, there exists a high correlation between annotators perceptions of toxicity and signals of African American English (AAE). This bias in annotated training data and the tendency of machine learning models to amplify it
Current computer graphics research practices contain racial biases that have resulted in investigations into skin and hair that focus on the hegemonic visual features of Europeans and East Asians. To broaden our research horizons to encompass all of
Online hate is a growing concern on many social media platforms and other sites. To combat it, technology companies are increasingly identifying and sanctioning `hateful users rather than simply moderating hateful content. Yet, most research in onlin
Despite the evolution of norms and regulations to mitigate the harm from biases, harmful discrimination linked to an individuals unconscious biases persists. Our goal is to better understand and detect the physiological and behavioral indicators of i