When a recruiter first screens a resume, they do so for ~6 seconds. So what is it that they are seeking?
A job role can attract thousands of applicants. When recruiters initially screen applicants, they are looking for ‘short-hand’ clues to confirm a pre-existing judgement that ‘predicts’ success, like a particular degree or score. Without knowing it, this method adversely affects the ability to hire the best candidates.
There is a better way.
FirstInterview is a true blind assessment . No demographic details are collected from candidates nor used to influence their ranking. Only the candidates answer to relevant interview questions are analysed by our scientifically validated algorithm to assess their fit for the role.
People are so much more than their CV, yet favouring a name, gender or institute over the individual is a common practice.
We cannot build inclusive industries when we don’t take steps to remove unconscious bias in our decisions when we are hiring. Being aware of our bias is one thing; removing it is another entirely.
It starts with a conversation. And a fair go.
Using FirstInterview means everyone gets the chance to do an interview and an opportunity to tell their story.
Algorithms and Ai learn according to the profile of the data we feed it.
If the data it learns from is taken from a CV, it’s only going to amplify our existing biases. Only clean data, like the answers to specific job-related questions, can give us a true bias-free outcome.
We continuously test the data that trains the machine so that if ever the slightest bias is found, it can be corrected.
Here's the science >
"Is there adverse
impact in our
recommendations?"
"Are the assessment score distributions similar across groups of interest?"
"Is the assessment making the same rate of errors across groups of interest?"
It’s hard to detect gaming in multi-choice personality assessments, nor can you detect plagiarism.
Ai is a super detector
Anomalies in answers are detected and flagged, including those that contain:
Here are our latest articles on reducing bias, removing discrimination
and building inclusive workforce through better hiring.
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