An external stability audit framework to test the validity of personality prediction in AI hiring

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers’ resumes or social media pro...

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Bibliographic Details
Published in:Data mining and knowledge discovery Vol. 36; no. 6; pp. 2153 - 2193
Main Authors: Rhea, Alene K., Markey, Kelsey, D’Arinzo, Lauren, Schellmann, Hilke, Sloane, Mona, Squires, Paul, Arif Khan, Falaah, Stoyanovich, Julia
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2022
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Audits
Chemistry and Earth Sciences
Computer Science
Data Mining and Knowledge Discovery
Information Storage and Retrieval
Personality
Personality tests
Personality traits
Physics
Psychological tests
Quantitative psychology
Reliability aspects
Special Issue on Bias and Fairness
Stability
Statistics for Engineering
Validity
Hiring
Validity
Personality
Stability
Reliability
Algorithm Audit
ISSN:1384-5810, 1573-756X
Online Access:Get full text
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Summary:Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers’ resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Crucially, rather than challenging or affirming the assumptions made in psychometric testing — that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job — we frame our audit methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems. This contribution is supplemented with an open-source software library that implements the technical components of the audit, and can be used to conduct similar stability audits of algorithmic systems. We instantiate our framework with the audit of two real-world personality prediction systems, namely, Humantic AI and Crystal. The application of our audit framework demonstrates that both these systems show substantial instability with respect to key facets of measurement, and hence cannot be considered valid testing instruments.
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Responsible editor: Toon Calders.
ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-022-00861-0