Addressing the so-called validity–diversity trade-off: Exploring the practicalities and legal defensibility of Pareto-optimization for reducing adverse impact within personnel selection

AbstractIt is necessary for personnel selection systems to be effective, fair, and legally appropriate. Sometimes these goals are complementary, whereas other times they conflict (leading to the so-called “validity-diversity dilemma”). In this practice forum, we trace the history and legality of proposed approaches for simultaneously maximizing job performance and diversity through personnel selection, leading to a review of a more recent method, the Pareto-optimization approach. We first describe the method at various levels of complexity and provide guidance (with examples) for implementing the technique in practice. Then, we review the potential points at which the method might be challenged legally and present defenses against those challenges. Finally, we conclude with practical tips for implementing Pareto-optimization within personnel selection.

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Performance and cost trade-off in Tracking Area reconfiguration: A Pareto-optimization approach

Computer Networks ◽

10.1016/j.comnet.2011.08.017 ◽

2012 ◽

Vol 56 (1) ◽

pp. 157-168 ◽

Cited By ~ 13

Author(s):

Sara Modarres Razavi ◽

Di Yuan ◽

Fredrik Gunnarsson ◽

Johan Moe

Keyword(s):

Pareto Optimization ◽

Optimization Approach ◽

Trade Off

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A Juggling Act: Devising Personnel Selection Systems

PsycEXTRA Dataset ◽

10.1037/e518572013-153 ◽

2006 ◽

Author(s):

Jamie L. Bomer ◽

Nicole R. Bourdeau ◽

Nita R. French ◽

Michael Klein ◽

Ryan A. Ross ◽

...

Keyword(s):

Personnel Selection ◽

Selection Systems

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Interrelationship Among Competition, Diversification and Liquidity Creation: Evidence from Indian Banks

Margin The Journal of Applied Economic Research ◽

10.1177/0973801021990398 ◽

2021 ◽

Vol 15 (2) ◽

pp. 183-204

Author(s):

Pankaj Sinha ◽

Naina Grover

Keyword(s):

Inverse Relationship ◽

Consumer Welfare ◽

Adverse Impact ◽

Public And Private ◽

Jel Codes ◽

Trade Off ◽

Private Banks ◽

Indian Banks ◽

The Impact

This study analyses the impact of competition on liquidity creation by banks and investigates the dynamics between diversification, liquidity creation and competition for banks operating in India during the period from 2005 to 2018. Using the broad and narrow measures of liquidity creation, an inverse relationship is determined between liquidity creation and competition. The study also indicates a trade-off between pro-competitive policies to improve consumer welfare and the liquidity-destroying effects of competition, and it highlights how diversification affects liquidity creation. Highly diversified banks in India create less liquidity compared with less-diversified banks, both public and private. The liquidity-destroying effects of competition is intensified among highly diversified private banks, which suggest that diversification has not moderated the adverse impact of competition. JEL Codes: G01, G18, G21, G28

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Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽

10.3390/a12050099 ◽

2019 ◽

Vol 12 (5) ◽

pp. 99 ◽

Cited By ~ 2

Author(s):

Kleopatra Pirpinia ◽

Peter A. N. Bosman ◽

Jan-Jakob Sonke ◽

Marcel van Herk ◽

Tanja Alderliesten

Keyword(s):

Machine Learning ◽

Image Registration ◽

State Of The Art ◽

Deformable Image Registration ◽

Optimization Approach ◽

High Quality ◽

Trade Off ◽

Multi Objective ◽

Current State ◽

Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

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Weighting Admission Scores to Balance Predictiveness‐Diversity: The Pareto‐Optimization Approach

Medical Education ◽

10.1111/medu.14606 ◽

2021 ◽

Author(s):

Filip Lievens ◽

Paul R. Sackett ◽

Wilfried De Corte

Keyword(s):

Pareto Optimization ◽

Optimization Approach

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Psychological Factors Related to Employees' Theft in the Convenience Store Industry

Psychological Reports ◽

10.2466/pr0.1982.51.3f.1219 ◽

1982 ◽

Vol 51 (3_suppl) ◽

pp. 1219-1238 ◽

Cited By ~ 56

Author(s):

William Terris ◽

John Jones

Keyword(s):

Personnel Selection ◽

Psychological Factors ◽

Adverse Impact ◽

Convenience Store ◽

Counterproductive Behavior ◽

Protected Group ◽

Behavior Study ◽

Industry Study ◽

Financial Need ◽

Thrill Seeking

Four studies are presented that examine various aspects of theft in the convenience store industry. Study 1 was a survey of both managers' ( n = 24) and retail clerks' ( n = 54) opinions on how and why convenience store employees steal. Results showed that the most frequently used theft techniques involved various ways of stealing cash from a register. Major reasons for employees' theft included financial need, low wages, revenge, and thrill-seeking. Major perceptions about why some employees never steal included fear of apprehension and personal honesty. Study 2 ( N = 61) showed that convenience store employees with more tolerant attitudes toward theft and violence, as measured by a pre-employment psychological test, the Personnel Selection Inventory, were more likely to engage in theft and other types of counterproductive behavior. Study 3 showed that the use of the inventory for 19 months by a 30-unit convenience store chain, for the purpose of screening out potential employee thieves and other counterproductive employees, was reliably more effective in reducing company shrinkage than a pre-employment polygraph procedure that was used for 23 months. Finally, Study 4 showed that the inventory had no adverse impact upon any protected group. Implications of these findings are discussed.

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A LEXICOGRAPHIC OPTIMIZATION APPROACH FOR BERTH SCHEDULE RECOVERY PROBLEM IN CONTAINER TERMINALS

Transport ◽

10.3846/16484142.2016.1125387 ◽

2016 ◽

Vol 31 (1) ◽

pp. 76-83 ◽

Cited By ~ 3

Author(s):

Qian Zhang ◽

Qingcheng Zeng ◽

Hualong Yang

Keyword(s):

Container Terminals ◽

Optimization Approach ◽

Technical Problems ◽

Trade Off ◽

Practical Applications ◽

Lexicographic Optimization ◽

Multi Stage ◽

Schedule Recovery ◽

Equipment Failures ◽

Recovery Problem

In container terminals, the planned berth schedules often have to be revised because of disruptions caused by severe weather, equipment failures, technical problems and other unforeseen events. In this paper, the problem of berth schedule recovery is addressed to reduce the influences caused by disruptions. A multi-objective, multi-stage model is developed considering the characteristics of different customers and the trade-off of all parties involved. An approach based on the lexicographic optimization is designed to solve the model. Numerical experiments are provided to illustrate the validity of the proposed Model A and algorithms. Results indicate that the designed Model A and algorithm can tackle the berth plan recovery problem efficiently because the beneficial trade-off among all parties involved are considered. In addition, it is more flexible and feasible with the aspect of practical applications considering that the objective order can be adjusted by decision makers.

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