Predictive analytics can help remove human (unconscious) bias from shortlisting to ensure diversity and inclusion – but only if it’s done right.
A 2018 article on Australia’s HRM website showcased a glaring example of racial bias in recruitment. It also highlighted more subtle problems, including the fact that ‘whitening’ a resume increases the likelihood of a callback.
It’s here that data analytics has a clear advantage. Done well, it can objectively predict crucial behaviours and outcomes about job applicants: including those most likely to be high-performing, low-risk staff ‘on the job’.
By using concrete data to screen candidates at the early stages, organisations can save time and money, pinpoint the right people to interview – and avoid human bias and guesswork at the earliest stages.
But there’s a catch. There’s a growing fear that biases will creep in anyway, because human bias will influence the algorithm itself.
And that’s the key: whoever creates the frameworks must know how to avoid bias. They must be specialists who can identify and rectify data distortions – and ensure discriminatory assumptions aren’t embedded from the very beginning.
Predikt-r is the breakthrough new tool that does exactly that.
Developed by qualified psychologists and experienced business performance and data specialists, it uses machine learning and unique algorithms to assess and rank job candidates.
Applicants do a validated, role-specific online assessment as the fist step. Then Predikt-r’s built-in scientific precision pinpoints best-fit individuals – with a single score for instant and objective shortlisting.
It means recruiters can move straight on to interviewing the people they already know will perform best in the workplace, saving time and providing concrete business benefits.
As a blended solution, Predikt-r doesn’t rely on one input (or one trait type): it combines multiple measures, such as mindset, ability and preference for robust predictions and rigorous validity.
In the end, data analytics alone doesn’t eliminate bias. There is a science to getting it right – and that calls for specialist know-how, clarity and a trained eye for potential pitfalls.
Want to get predictive employment screening data analytics right from the start? Talk to the team at Predikt-r.