Personnel selection: A review of ways to maximize validity, diversity, and the applicant experience
Personnel Psychology has a long tradition of publishing important research on personnel
selection. In this article, we review some of the key questions and findings from studies …
selection. In this article, we review some of the key questions and findings from studies …
Well-being: The ultimate criterion for organizational sciences
For too long, organizational science has implicitly or explicitly endorsed job performance as
the ultimate criterion (or the bottom line for organizational performance). We propose that a …
the ultimate criterion (or the bottom line for organizational performance). We propose that a …
A conceptual framework for investigating and mitigating machine-learning measurement bias (MLMB) in psychological assessment
Given significant concerns about fairness and bias in the use of artificial intelligence (AI) and
machine learning (ML) for psychological assessment, we provide a conceptual framework …
machine learning (ML) for psychological assessment, we provide a conceptual framework …
Whither bias goes, I will go: An integrative, systematic review of algorithmic bias mitigation.
Abstract Machine learning (ML) models are increasingly used for personnel assessment and
selection (eg, resume screeners, automatically scored interviews). However, concerns have …
selection (eg, resume screeners, automatically scored interviews). However, concerns have …
Reducing subgroup differences in personnel selection through the application of machine learning
Researchers have investigated whether machine learning (ML) may be able to resolve one
of the most fundamental concerns in personnel selection, which is by hel** reduce the …
of the most fundamental concerns in personnel selection, which is by hel** reduce the …
The Effects of the Training Sample Size, Ground Truth Reliability, and NLP Method on Language-Based Automatic Interview Scores' Psychometric Properties
While machine learning (ML) can validly score psychological constructs from behavior,
several conditions often change across studies, making it difficult to understand why the …
several conditions often change across studies, making it difficult to understand why the …
Insights from an updated personnel selection meta-analytic matrix: Revisiting general mental ability tests' role in the validity–diversity trade-off.
General mental ability (GMA) tests have long been at the heart of the validity–diversity trade-
off, with conventional wisdom being that reducing their weight in personnel selection can …
off, with conventional wisdom being that reducing their weight in personnel selection can …
Adverse impact reduction and job performance optimization via pareto-optimal weighting: A shrinkage formula and regularization technique using machine learning.
In personnel selection practice, one useful technique for reducing adverse impact and
enhancing diversity is the Pareto-optimal weighting approach of De Corte et al.(2007). This …
enhancing diversity is the Pareto-optimal weighting approach of De Corte et al.(2007). This …
Drop** the GRE, kee** the GRE, or GRE-optional admissions? Considering tradeoffs and fairness
In considering whether to retain the GRE in graduate school admissions, admissions
committees often pursue two objectives:(a) performance in graduate school (eg, admitting …
committees often pursue two objectives:(a) performance in graduate school (eg, admitting …
Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A case study of automated video interviews
We provide a psychometric-grounded exposition of bias and fairness as applied to a typical
machine learning (ML) pipeline for affective computing (AC). We expand on an interpersonal …
machine learning (ML) pipeline for affective computing (AC). We expand on an interpersonal …