Enforcing the Equal Protection Clause: Congressional Power, Judicial Doctrine, and Constitutional Law by William D.Araiza. New York, New York University Press, 2016. 336 pp. $65.00.

2017 ◽  
Vol 132 (2) ◽  
pp. 393-394
Author(s):  
Jeb Barnes
Keyword(s):  
New York ◽  
Constitutional Law ◽  
Equal Protection ◽  
New York University ◽  
Equal Protection Clause ◽  
Judicial Doctrine

O que Brown e Obergefell nos Ensinam sobre Contramajoritarismo? / What do Brown and Obergefell Teach us about Countermajoritarism?

2017 ◽  
Author(s):  
Henrique Rangel ◽  
Carlos Bolonha ◽  
Igor De Lazari
Keyword(s):  
Decision Making ◽  
Constitutional Law ◽  
Status Quo ◽  
Equal Protection ◽  
Board Of Education ◽  
Equal Protection Clause ◽  
American States ◽  
Qualified Majority ◽  
Social Minorities ◽  
The U.S

O presente artigo propõe uma comparação entre Brown vs. Board of Education (1954) e Obergefell vs. Hodges (2015). A primeira decisão, por ser sensivelmente abstrata e influente no direito constitucional norte-americano, passou por um processo marcante de mitificação acerca de seu caráter contramajoritário. A segunda também protegeu uma minoria social com base na interpretação da cláusula de proteção igualitária e, assim, pode ser alvo desse mesmo fenômeno. Diante desse processo de mitificação, sustenta-se a hipótese de que há natureza majoritarista nas decisões de Brown e Obergefell. Embora ambas as decisões tenham protegido direitos de minorias sociais, tais mudanças interpretativas foram adiadas pela Suprema Corte norte-americana até que uma maioria qualificada de Estados já as tivesse implementado. A metodologia se baseou em dois parâmetros: o comportamento decisório da Corte e o status quo constitucional da matéria, representado pelo número de Estados favoráveis ou contrários a determinada interpretação constitucional. AbstractThis article proposes a comparison between Brown vs. Board of Education (1954) and Obergefell vs. Hodges (2015). The first decision is significantly abstract and influential in American constitutional law. In this sense, it has passed through a remarkable process of mystification about its countermajoritarian character. The second one also has protected a social minority based on the interpretation of the equal protection clause. Thus, it is supposed to be aimed by the same phenomenon. Before this mystification process, this article supports the follow hypothesis: Brown and Obergefell decisions indicate a majoritarian profile. Both decisions have protected rights of social minorities, but the U.S. Supreme Court has delayed such interpretative changes until a qualified majority of the American States has already provided it. The methodology consists of two analytical parameters: the decision-making of the Court and the constitutional status quo of the matter defined by the number of States favorable or contrary to certain constitutional interpretation.

scholarly journals Equal Protection Under Algorithms: A New Statistical and Legal Framework

2020 ◽  
pp. 291
Author(s):  
Crystal Yang ◽  
Will Dobbie
Keyword(s):  
New York ◽  
Legal Framework ◽  
Criminal History ◽  
Equal Protection ◽  
Equal Protection Clause ◽  
Statistical Framework ◽  
Predictive Algorithms ◽  
Legal Position ◽  
Black Defendants ◽  
Statistical Solutions

In this Article, we provide a new statistical and legal framework to understand the legality and fairness of predictive algorithms under the Equal Protection Clause. We begin by reviewing the main legal concerns regarding the use of protected characteristics such as race and the correlates of protected characteristics such as criminal history. The use of race and nonrace correlates in predictive algorithms generates direct and proxy effects of race, respectively, that can lead to racial disparities that many view as unwarranted and discriminatory. These effects have led to the mainstream legal consensus that the use of race and nonrace correlates in predictive algorithms is both problematic and potentially unconstitutional under the Equal Protection Clause. This mainstream position is also reflected in practice, with all commonly used predictive algorithms excluding race and many excluding nonrace correlates such as employment and education. Next, we challenge the mainstream legal position that the use of a protected characteristic always violates the Equal Protection Clause. We develop a statistical framework that formalizes exactly how the direct and proxy effects of race can lead to algorithmic predictions that disadvantage minorities relative to nonminorities. While an overly formalistic solution requires exclusion of race and all potential nonrace correlates, we show that this type of algorithm is unlikely to work in practice because nearly all algorithmic inputs are correlated with race. We then show that there are two simple statistical solutions that can eliminate the direct and proxy effects of race, and which are implementable even when all inputs are correlated with race. We argue that our proposed algorithms uphold the principles of the equal protection doctrine because they ensure that individuals are not treated differently on the basis of membership in a protected class, in stark contrast to commonly used algorithms that unfairly disadvantage minorities despite the exclusion of race. We conclude by empirically testing our proposed algorithms in the context of the New York City pretrial system. We show that nearly all commonly used algorithms violate certain principles underlying the Equal Protection Clause by including variables that are correlated with race, generating substantial proxy effects that unfairly disadvantage Black individuals relative to white individuals. Both of our proposed algorithms substantially reduce the number of Black defendants detained compared to commonly used algorithms by eliminating these proxy effects. These findings suggest a fundamental rethinking of the equal protection doctrine as it applies to predictive algorithms and the folly of relying on commonly used algorithms.

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