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What can Ai help us discover?

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What can Ai help us discover?

We are asked often in customer meetings – What does the data reveal?

What can AI help us discover? How can we make better people and business decisions by looking at the data?

By using SOM maps https://en.wikipedia.org/wiki/Self-organizing_map to map personality for more than 85,000 applicants using their HEXACO scores, 47/53% male and female, candidates spread across 2 regions – the UK and Australia, we identified 400 unique personality profiles.

It turns out that personality is somewhat more complex than the 16 types long promoted by Myers Briggs.

Following SOM’s show the percentage density of male, female and sales candidates across the 400 different HEXACO profile groups. The size of each bubble represents the total count of individuals mapped to each profile. Darker shades represent higher % of each category.

The ‘so what’ …

People are different. There is a whole spectrum of personality. Even 400 groupings of personality don’t convey the variance in personality in the population
Gender is less meaningful as a lens into personality than what role family you sit in- the genders are not that different when it comes to personality profiling
Role family differences are noticeable. Gender personality differences at the role family level are negligible. “Sales” candidates come from specific personality profiles (i.e. HEXACO combinations) and it is clear that certain personality profiles are drawn to sales more than others. See the SOM on Sales that clearly shows some profiles are more likely to be sales candidates than others (the darker bubbles).

Why natural language is a better approach to discovering personality traits

Personality is widely accepted as an indicator of job performance. Until now, the only way to accurately measure personality was through long and repetitive 100+ item personality tests, where the candidate experience is proven to be weak. The PredictiveHire team breaks new ground disrupting decades of assessment practice by showing that answers to standard interview questions related to past behaviour and situational judgement can be used to reliably infer personality traits. Leveraging natural language processing, machine learning and personality theory, we validate that text is a reliable indicator of hidden personality traits. Additionally, this approach to candidate interviews is blind to gender, race and any other characteristics that are not directly relevant in job selection. Instead, every applicant is given a fair opportunity to express themselves and be evaluated equally.

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