Quantitative measures of discrimination with application to appointment processes
P. A. Robinson, C. C. Kerr
TL;DR
This paper introduces statistical methods to measure bias in appointment processes and applies them to show sex and racial discrimination in Australian universities.
Contribution
A novel statistical framework and webapp for quantifying discrimination in appointment processes using the binomial distribution.
Findings
Significant sex discrimination was found in the appointment of university chief executives in 2018.
Extreme racial discrimination was identified in senior appointments across major Australian universities as of 2021.
Racially unbiased outcomes are unlikely without significant changes to current selection procedures.
Abstract
Bias and discrimination in appointment processes such as hiring decisions (and analogous selection procedures for performance evaluations, promotions, scholarships, and awards), are quantified statistically via the binomial distribution. These statistical measures are described and an easily used webapp for calculating them is provided. The measures considered include the likelihood that a given number of appointments arose from a fair process and the likelihood that an existing process would give rise to a fair outcome if it were repeated. These methods are illustrated by applying them to sex (including gender) discrimination and racial discrimination in senior appointments in the Australian university sector; both conscious and unconscious biases are included. Significant sex discrimination is found to have existed in the appointments of university chief executives (Vice Chancellors)…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsGender Diversity and Inequality · Names, Identity, and Discrimination Research · Labor market dynamics and wage inequality