Level the playing field with processes rooted in science and driven by data

We help reduce unconscious bias during the hiring process in three specific ways: 

1. Our magic quadrant research methodology shows that behavioural assessments increase diversity without sacrificing performance prediction in comparison to other assessment types, such as cognitive ability assessments. Although behavioural assessments can be expensive and time-consuming, we’ve automated the process which allows you to use them earlier on in the recruitment process instead of other selection procedures known to negatively impact diversity.  
2. We reduce recruiter bias by ensuring every candidate gets exactly the same assessment. We found that unstructured interviews allow for human bias to creep its way into the selection process. By using a structured behavioural interview process, we use this automation to ensure that none of your typical bias is present, which gives every candidate a fair chance in the selection process. 
3. We train our machine learning to score candidates through objective data that has not been subject to human biases. In data science, there is a general understanding of what happens when the data used to train models is biased — garbage in, garbage out.  Since machine learning models learn from data that we feed to them, any bias that exists in that data will translate through the use of machine learning. This is why we ensure our machine learning is only taught objective data that hasn’t been subject to human biases, and does not include historical hiring patterns.