Diversity hiring - the solution is in data patterns

AI helps you discover the right patterns without bias.

Good pattern recognition allows you to make better decisions, short-circuit lengthy processes, avoid mistakes, and better understand risks.

But it has a downside too. Just because you can see a pattern in what has gone before, it is no guarantee that those same things will be true in the future.

Pattern recognition produces particularly flawed results in the hiring process.

When you hear hoofbeats, it’s probably horses. But you never know when it might be a zebra.

We all want to hire people like us, but true innovation comes through diversity.

Recruiters know that they should strip-out any markers that trigger unconscious bias when interviewing – but unconscious bias is hard to fight. The only way to remove those markers is via technology.

AI helps you discover the right patterns without bias.


How does AI solve discrimination and bias issues in recruitment?

Every role has a unique profile and every person has their own unique personality and aptitude DNA. We use a combination of natural language processing (NLP), a branch of AI-specific to text data and machine learning to predict with 85%+ accuracy if someone is right for a role.

NLP provides methods to program computers to process and analyse large amounts of human language data. It takes many forms, but at its core, it’s about communication, but we all know words run much deeper than that. There is a context that we derive from everything someone says.

Google, Facebook, IBM Watson are technologies that also rely on NLP to comb through large amounts of text data. The end result is insights and analysis that would otherwise either be impossible or take far too long.


Busting the stereotypes with interview data

Women are more conscientious than men in their text interview.

Men make on average 4.5% more language errors than women while taking 2% more time on average than women.

Interestingly men show higher levels of English fluency using more difficult words than their female counterparts, more than 4.5% on average.

These stats fluctuate depending on the role. For example, when applying for customer service roles, women take 6% more time than men while making 5% fewer language errors (language errors include grammar and spelling errors).

Women use more words on average in their text interview than men. We don’t find this to be the case.

Who writes more depends on the role family, but we find the difference to be +/- 2% on average (effect size, a more accurate way to measure the difference in averages is less than 0.2 across all role families. This is considered small). For example, in Graduate roles, men write more and in sales and hospitality roles females write more, while answering the same interview questions.

Our data shows that more extraverted candidates are preferred at the hiring stage for sales roles.

On average a hired candidate is 7 percentile points higher in extraversion than the candidate population average. As we track new hire performance in their first 12 months and beyond, we are starting to see a different profile turning up in the better sales performers – more introverts.


If you want to learn more about how we get to these insights from our FirstInterview AI screening tool get in touch here.


In sales, your single-minded focus on targets is far more important than how you present yourself. For recruiters who think otherwise, they may be operating with bias.



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