Recently, the personality AI startup has raised $1.5 Mn in its first round of external funding Humantic AI's long term goal remains to'humanise the internet' by expanding its core product to provide insights into all kinds of conversations Revenue teams are the heart and soul of every product-based company since they bring in the money to actually run the business in the first place. However, as it is right now, sales is grunt work, with long meetings with no real communication, just two teams talking. Companies, therefore, have tried to ease the grunt work that remains with the sales teams; the most promising of this is using assistive AI to augment and automate the entire sales process. However, a gap still exists, in terms of personalisation of the conversations during the deals. To bring more of the'human' factor in, Frrole AI founder Amarpreet Kalkat founded Humantic AI in 2019.

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. Our approach is to (a) develop a methodology for an external audit of stability of predictions made by algorithmic personality tests, and (b) instantiate this methodology in an audit of two systems, Humantic AI and Crystal. 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 methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. In our audit of Humantic AI and Crystal, we find that both systems show substantial instability with respect to key facets of measurement, and so cannot be considered valid testing instruments. For example, Crystal frequently computes different personality scores if the same resume is given in PDF vs. in raw text format, violating the assumption that the output of an algorithmic personality test is stable across job-irrelevant variations in the input. Among other notable findings is evidence of persistent -- and often incorrect -- data linkage by Humantic AI.