Machine-Learning Credit Scores and Disparate Impact Theory

2018 ◽  
Author(s):  
Lauri Kai
Keyword(s):  
Machine Learning ◽  
Disparate Impact ◽  
Impact Theory ◽  
Credit Scores

Fetal Protection and Potential Liability: Judicial Application of the Pregnancy Discrimination Act and the Disparate Impact Theory

1985 ◽  
Vol 11 (3) ◽  
pp. 369-390
Author(s):  
Laurence S. Moelis
Keyword(s):  
Employment Discrimination ◽  
Judicial Interpretation ◽  
Disparate Impact ◽  
Hazardous Material ◽  
Protective Measures ◽  
Fetal Protection ◽  
Pregnancy Discrimination ◽  
Impact Theory ◽  
Pregnancy Discrimination Act

Abstract“Fetal vulnerability programs,” which are employer attempts to protect employees' unborn fetuses from harm caused by the mothers' exposure to hazardous material in the workplace, have been challenged as a form of employment discrimination. This Note analyzes the recent judicial application of the Pregnancy Discrimination Act (PDA) and the disparate impact theory to fetal vulnerability cases. The Note also examines the business necessity defense's accommodation of legitimate employer interests. The Note concludes that a more potent business necessity defense, a stricter standard for evaluating alternative protective measures, and a judicial interpretation of the PDA which is more consistent with congressional intent are necessary for fair and reasonable resolution of these cases.

THE RESURRECTION OF THE DISPARATE IMPACT THEORY?

1991 ◽  
Vol 29 (3) ◽  
pp. 451-458 ◽  
Author(s):  
PATRICIA PATTISON ◽  
PHILIP E. VARCA
Keyword(s):  
Disparate Impact ◽  
Impact Theory

THE DEMISE OF THE DISPARATE IMPACT THEORY

1991 ◽  
Vol 29 (3) ◽  
pp. 413-450
Author(s):  
PATRICIA PATTISON ◽  
PHILIP E. VARCA
Keyword(s):  
Disparate Impact ◽  
Impact Theory

Encouraging Debate on the Uniform Guidelines and the Disparate Impact Theory of Discrimination

2011 ◽  
Vol 4 (4) ◽  
pp. 566-570 ◽  
Author(s):  
Michael A. McDaniel ◽  
Sven Kepes ◽  
George C. Banks
Keyword(s):  
Racial Differences ◽  
Personnel Selection ◽  
Expert Witness ◽  
Employee Selection ◽  
Disparate Impact ◽  
Witness Testimony ◽  
Expert Witness Testimony ◽  
Impact Theory ◽  
Long Time

This response summarizes commentaries on the M. A. McDaniel, S. Kepes, and G. C. Banks (2011) article, which argued that the Uniform Guidelines on Employee Selection Procedures are a detriment to the field of personnel selection. Several themes were present in the commentaries. No compelling arguments were presented to dispute the assertion that mean racial differences in job-related attributes will be with us for a long time. However, compelling arguments were made that the disparate impact theory of discrimination is a more central issue for personnel selection than the Uniform Guidelines. Similarly, arguments were presented that the assessment of adverse impact is problematic and that expert witness testimony needs improvement. Areas in need of further investigation were also identified. Finally, the role of the Society of Industrial and Organizational Psychology (SIOP) in guiding regulatory, legislative, and court actions was considered.

scholarly journals When Home is a Living Hell

2020 ◽  
Vol 6 (4) ◽  
Author(s):  
Sarah Pricer
Keyword(s):  
Sexual Harassment ◽  
Affordable Housing ◽  
Low Income ◽  
State Level ◽  
Housing Crisis ◽  
Federal Law ◽  
Disparate Impact ◽  
Impact Theory ◽  
Low Income Women ◽  
Texas Women

Low-income women experience a nightmarish victimization when they are sexually harassed by landlords in their homes, homes many are desperate to keep. The staggering lack of data on this issue means laws and courts have been slow to address this phenomenon. Although courts have relied primarily on a Title VII employment-based sexual harassment framework to address this issue, it does not go far enough in protecting women in their homes. The home and the workplace are inherently different and thus require a different legal approach to redress the issue. This comment examines sexual harassment in housing and why Texas women are particularly susceptible to it. This comment further advocates for courts to look to existing housing law and expand application of the Fair Housing Act’s disparate impact approach in addressing sexual harassment in housing to low-income women. Low- income women of color are the primary victims of sexual harassment in housing; this permits a gender and race-based disparate impact theory as another framework through which courts may craft a solution, though it too has its shortcomings. Additional measures, such as the expansion of federal law to cover sexual harassment in housing, and state-level policy changes that would penalize deviant landlords and property owners, would serve to empower and strengthen low- income Texas women without leaving entire subsections of the population behind. This comment further argues that holistic community-level action is not only desirable, but necessary to educate communities and provide low-income women increased access to the courts. Finally, the above suggested measures will do little to change the outlook for low-income women if states fail to address the underlying issue that makes the need for housing so desperate: the affordable housing crisis.

An Empirical Study on Loan Default Prediction Models

2019 ◽  
Vol 16 (8) ◽  
pp. 3483-3488
Author(s):  
Uzair Aslam ◽  
Hafiz Ilyas Tariq Aziz ◽  
Asim Sohail ◽  
Nowshath Kadhar Batcha
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Prediction Models ◽  
Historical Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Loan Default ◽  
Default Prediction ◽  
The World ◽  
Credit Scores

Loan lending has been playing a significant role in the financial world throughout the years. Although it is quite profitable and beneficial for both the lenders and the borrowers. It does, however, carry a great risk, which in the domain of loan lending is referred to as Credit risk. Industry experts and Researchers around the world assign individuals with numerical scores known as credit scores to measure the risk and their creditworthiness. Throughout the years machine learning algorithms have been used to calculate and predict credit risk by evaluating an individual’s historical data. This study reviews the present literature on models predicting risk assessment that use machine learning algorithms.

Diversity, disparate impact, and discrimination pursuant to Title VII of US civil rights laws

2017 ◽  
Vol 36 (7) ◽  
pp. 670-691
Author(s):  
Frank J. Cavico ◽  
Bahaudin Mujtaba
Keyword(s):  
Civil Rights ◽  
Equal Employment Opportunity Commission ◽  
Salient Feature ◽  
Specific Aspect ◽  
Title Vii ◽  
Employment Policy ◽  
Disparate Impact ◽  
Content Type ◽  
Impact Theory ◽  
Disparate Treatment

Purpose While the words diversity, disparate impact, and discrimination are commonly read and heard by working adults and professionals, they can at times be confusing and fearful to some managers. The purpose of this paper is to provide an overview of a specific aspect of US civil rights laws – the disparate impact theory. The authors provide an analysis based on the statute, case law interpreting, and applying the statute, administrative guidelines from the Equal Employment Opportunity Commission, as well as legal and management commentary. The paper illustrates the requirements of a plaintiff employee’s initial case based on the disparate impact theory. The challenging causation component which requires some degree of statistical evidence is given particular attention. Limitations to the paper are stated at the beginning; and recommendations to managers are explored and provided toward the end of the paper. Design/methodology/approach It is a legal paper which covers all the laws related to discrimination based on disparate impact and disparate treatment theories. Actual court cases up until this month and Americans laws related to this concept are reviewed and critically discussed. Findings The salient feature of disparate impact is that this legal theory allows a plaintiff job applicant or employee to sustain a case of illegal discrimination without providing any evidence of a discriminatory motive. As opposed to the disparate treatment liability is imposed based on disproportionate adverse results and not discriminatory intent. Research limitations/implications This paper deals with the disparate impact theory pursuant to Title VII of the Civil Rights Act. However, it must be pointed out that the disparate impact theory is also applicable to claims arising under the Americans with Disabilities Act and the Age Discrimination in Employment Act. Since the focus of this paper is Title VII federal and state constitutional issues, such as the applicability of the 14th Amendment’s Equal Protection clause that may arise in disparate impact cases involving government entities will not be addressed. Practical implications Managers and employees can protect themselves in the workplace from illegal discriminatory practices. Initially, employers and managers must be aware of the distinction between a disparate impact case and a disparate treatment case with the latter requiring evidence of intentional discrimination. Evidence, of course, can be direct or circumstantial or inferential. Whereas in a disparate impact case there is no intentional discrimination; and as such proof of discriminatory intent is not required. Rather, the employee has to present evidence that the employer’s neutral on-its-face employment policy or practice caused an adverse disproportionate impact on the employee as a member of a protected class. Social implications Human resources professionals and managers must become educated in diversity laws in order to provide an inclusive workplace for all employees and candidates. Employers have legitimate areas of concern in hiring and promoting employees; and the courts are cognizant of employer responsibilities; and thus the employers must be able to show how specific knowledge, skills, education, training, backgrounds, as well as height, weight, strength, and dexterity are legitimate qualifications that directly relate to successful job performance. Originality/value This is an original paper by the authors.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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