Environmental Racism and Natural Disasters: Preparing for a Future Defined by Climate Change

ABSTRACT This paper argues that the negative effects of climate change induced natural disasters are felt disproportionately by poor and minority communities, and that it is more difficult for them to recover after crises. Because climate change has resulted in an increase in the frequency and severity of natural disasters and is only expected to get worse, disparate effects from natural disasters are a crucial topic to focus research on. This paper will expand the framework for future research in the field of environmental justice by establishing a focus on the intersection between global warming, natural disasters, and environmental racism. It will also illustrate the disparate impact of natural disasters on poor and minority communities with a series of case studies and will evaluate the government’s response in each case.

Enabling Equal Opportunity in Logistic Regression Algorithm

Research Question: This paper aims at adjusting the logistic regression algorithm to mitigate unwanted discrimination shown towards race, gender, etc. Motivation: Decades of research in the field of algorithm design have been dedicated to making a better prediction model. Many algorithms are designed and improved, which made them better than the judgments of people and even experts. However, in recent years it has been discovered that predictive models can make unwanted discrimination. Such unwanted discrimination in the predictive model can lead to legal consequences. In order to mitigate the problem of unwanted discrimination, we propose equal opportunity between privileged and discriminated groups in the logistic regression algorithm. Idea: Our idea is to add a regularization term in the goal function of the logistic regression. Therefore, our predictive model will solve both the social problem and the predictive problem. More specifically, our model will provide fair and accurate predictions. Data: The data used in this research present U.S. census data describing individuals using personal characteristics with a goal to provide a binary classification model for predicting if an individual has an annual salary above $50k. The dataset used is known for disparate impact regarding female individuals. In addition, we used the COMPAS dataset aimed at predicting recidivism. COMPAS is biased toward African-Americans. Tools: We developed a novel regularization technique for equal opportunity in the logistic regression algorithm. The proposed regularization is compared against classical logistic regression and fairness constraint logistic regression, using a ten-fold cross-validation. Findings: The results suggest that equal opportunity logistic regression manages to create a fair prediction model. More specifically, our model improved both disparate impact and equal opportunity compared to classical logistic regression, with a minor loss in prediction accuracy. Compared to the disparate impact constrained logistic regression, our approach has higher prediction accuracy and equal opportunity, while having a lower disparate impact. By inspecting the coefficients of our approach and classical logistic regression, one can see that proxy attribute coefficients are reduced to very low values. Contribution: The main contribution of this paper is in the methodological part. More specifically, we implemented an equal opportunity in the logistic regression algorithm.

Bias Mitigation in Data Sets

Tackling sample bias, Non-Response Bias, CognitiveBias, and disparate impact associated with Protected Categories in three parts/papers, data, algorithm construction, and output impact. This paper covers the Data section.

Personalized Law and Equal Protection

This chapter examines personalized law from the perspective of the Equal Protection Clause in the United States Constitution. Some classifications of people, when made for the purpose of differentiated treatment, are subject to stifling doctrinal constraints. Could such classifications be made under personalized law? The chapter argues that personalized law mitigates the constitutional concerns relating to suspect classifications. Treating people as individuals, using multi-attribute data-weighed tailoring, and not as identical members in a certain class, is permissible because members of the class are not singled out for class-specific uniform treatment. The chapter examines landmark Supreme Court cases on sex and race classifications, showing that the limits set by the Court and the narrow permission it granted for some uses of classifications, all fit well within a scheme of personalized law. In addition, the chapter examines problems of unintended disparate impact that could arise under personalized law, and demonstrates the unique advantage of the algorithmic methods fueling personalized law in reducing and eliminating such effects.

The Prediction of Youth Recidivism in a Spanish Roma Population by the Youth Level of Service/Case Management Inventory (YLS/CMI)

Despite the increasing interest in the accuracy of youth risk assessment tools, the amount of research with ethnic minorities remains relatively modest. For this reason, the main goal of this study was to assess the predictive validity and disparate impact of the Youth Level of Service/Case Management Inventory (YLS/CMI) in a Spanish ethnic minority. The participants consisted of 88 Roma youth offenders and 135 non-Roma youth offenders, aged between 14 and 17 years old. Their risk of recidivism was assessed by means of the YLS/CMI Inventory and their recidivism rate was obtained from the Juvenile Justice Department. Results showed that the Inventory presented slightly lower predictive validity for the Roma group. Moreover, Roma juveniles presented higher risk scores and lower strength scores than non-Roma juveniles. These results supported the idea that professionals must therefore be aware of these cultural differences in predictive validity and the existent potentiality for disparate impact.