Deliver gender de-biasing in recruitment with Oleeo.

Language is a key contributor to such bias – if you want to know more, this video shows how to eliminate it. There is much evidence published that shows that subtle but systematic wording differences can influence job appeal negatively and increase gender inequality. These differences also exist in candidate CVs or resumes, and perpetuate biases – either unconsciously through the human recruiter or through machine learning representations.

Eliminating gender bias needs to include technology that helps amplify blind recruitment. Oleeo has sought to deliver gender de-biasing in recruitment. We have done this by seeking to establish features that may differentiate a male candidate’s CV or resume from a female candidate’s resume. We have considered if there are lexical, syntactic and semantic differences in the text that distinguishes male and female CV or resumes and if these differences are then perpetuated or amplified in machine learning representations of the CV and resumes – this video explains it more.

Using Oleeo technology, recruiters can determine if disparate impact on the gender minority may occur in the hiring process due to statistical differences in the underlying male and female CVs or resumes, manifesting as redundant encodings of gender information in machine learning representations of CVs and resumes. This is why gender de-biasing in recruitment with Oleeo is paramount.

Oleeo analysis has demonstrated how small, but statistically significant patterns, such as resume length, readability and use of certain words can easily lead to gender identification and influence a recruiter looking to achieve gender balance subconsciously.