Deliver gender de-biasing in recruitment with Oleeo


Deliver gender de-biasing in recruitment with Oleeo.

This video gives you more information on what the tool offers to help you ensure gender de-biasing in recruitment with Oleeo.

Using Oleeo Technology

 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. Findings that influenced this product include:

Oleeo’s study shows that instead of weighing up different companies and limiting their applications to the employer brands they rate, candidates are applying a more scatter-gun approach, basing their decision to apply on minimum job requirements and criteria, such as location and salary. To combat the low rate of hire, candidates are applying to up to 30 different jobs and are not becoming invested in a particular brand at the early stages of the recruitment process – because the likelihood of receiving a response is slim.

Oleeo’s research suggests that this technology is a much needed support for recruiters who typically have 91 tasks to perform as part of the hiring process. However, it isn’t the only solution. To deal with a candidate community focused on the ‘job brand’, employers also need to review their hiring processes. Instead of having a static, one-size fits all approach, they need to implement multiple hiring funnels designed around the individual needs of specific roles, so that it’s much easier for candidates to see the specifics of a role, upfront. The report also shows that when employers implement multiple hiring funnels it allows them to target job seekers like a marketer targets consumers; tailoring the selection criteria and screening so that it’s appropriate for each job group, and allowing communication and content to be specific for each audience.

Whilst the statistical differences for CVs or resumes between different job sectors are very apparent, the differences in a specific job sector between male and female CVs or resumes was more subtle but still statistically significant. For instance, the number of sentences, words and unique words used in a CV or resume have distinct differences across job sectors.

However, there is a gender difference in that 90% of the top-10 male discriminant words are proper nouns and nouns, whereas only 68% of the top-10 female discriminant words are proper nouns and nouns. This indicates that proper nouns usage plays a big role in differentiating male and female CVs or resumes, which is not surprising as it encompasses given names which have gender connotations.

Even with blind recruitment methods applied, the redundant encoding of gender in machine learning representations could still be an issue and appears to be amplified, which could lead to disparate impact on the gender minority.


An add-on module to Oleeo Recruit, Intelligent Writing helps recruiters create gender-neutral job postings/descriptions, attracting more female candidates. Leveraging artificial intelligence, Intelligent Writing identifies gender biased words and recommends gender-neutral alternatives. This datasheet gives you more information on what the tool offers to help you ensure gender de-biasing in recruitment with Oleeo. Click the buttons below to view this short video or talk to us today about how we can assist with your gender de-biasing in recruitment needs.

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