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Automated hiring systems are among the fastest-developing of all high-stakesAI systems. Among these are algorithmic personality tests that use insightsfrom psychometric testing, and promise to surface personality traits indicativeof future success based on job seekers' resumes or social media profiles. Weinterrogate the validity of such systems using stability of the outputs theyproduce, noting that reliability is a necessary, but not a sufficient,condition for validity. Our approach is to (a) develop a methodology for anexternal audit of stability of predictions made by algorithmic personalitytests, and (b) instantiate this methodology in an audit of two systems,Humantic AI and Crystal. Crucially, rather than challenging or affirming theassumptions made in psychometric testing -- that personality is a meaningfuland measurable construct, and that personality traits are indicative of futuresuccess on the job -- we frame our methodology around testing the underlyingassumptions made by the vendors of the algorithmic personality teststhemselves.  Our main contribution is the development of a socio-technical framework forauditing the stability of algorithmic systems. This contribution issupplemented with an open-source software library that implements the technicalcomponents of the audit, and can be used to conduct similar stability audits ofalgorithmic systems. We instantiate our framework with the audit of tworeal-world personality prediction systems, namely Humantic AI and Crystal. Theapplication of our audit framework demonstrates that both these systems showsubstantial instability with respect to key facets of measurement, and hencecannot be considered valid testing instruments.

An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring