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 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.

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability.

In this perspective, we develop a matrix for auditing algorithmic decision-making systems (ADSs) used in the hiring domain. The tool is a socio-technical assessment of hiring ADSs that is aimed at surfacing the underlying assumptions that justify the use of an algorithmic tool and the forms of knowledge or insight they purport to produce. These underlying assumptions, it is argued, are crucial for assessing not only whether an ADS works "as intended," but also whether the intentions with which the tool was designed are well founded.