How blind reviews can improve gender balance
In a time when unconscious biases affect almost every aspect of the workplace, blind reviews offer a way to open up the female talent pipeline.
Research finds recruitment processes to be 85% ineffective. Traditional sourcing and identification methodologies tend to be riddled with the biases of the recruiter, HR representative and hiring manager. These biases can be unconscious or otherwise. Either way, diversity recruitment is highlighted as a top priority and trend by 78% of talent management professionals for 2018. The business case for gender balance is very compelling and should be a priority for all organisations, but it doesn’t appear that way much of the time. What is obvious is that organisations need well-conceived strategies to be in a better place to identify, source and hire increased numbers women.
Two potential strategies
There are two possible scenarios requiring different approaches:
- Target women candidates directly: This is about revamping outdated candidate sourcing practices and is best employed in sectors or for openings where female candidates are hard to identify. In these cases an organisation needs to pro-actively look for or encourage applications from women. It will be important to make every element of the hiring process female friendly to draw them in.
Download our free eBook for Recruiters and Hiring Manager to understand how men and women apply for jobs differently
- Manage the unconscious bias in existing systems: This is effective for existing systems where men and women apply in equal numbers, but female candidates are eliminated from the pipeline by the biases of those involved. To tackle this, fields which trigger bias are removed or covered. Masked or blind reviews are increasingly incorporated into the algorithms found in different specialist software designed for this purpose, which is then used by HRIS (Human Resource Information Systems) and ATS (Applicant Tracking Systems). The outcome is a better chance of gender balanced long lists.
Some organizations are tapping into technology in order to root out bias and so improve their diversity hiring results. Using blind reviews embedded in AI, candidate data is masked or extracted to mitigate against patterns of potential bias. By applying an automated and objective process, the scope of bias can be reduced at the sourcing and CV triage stage of the hiring process. This increases the chances of having a higher number of women at the long list stage, where otherwise a significant number of candidates are eliminated.
Key data that can be masked would include:
Name: Implicit in a person’s name is a candidate’s gender, race, ethnicity or cultural background.
Education: Educational level and the institution are taken out. Very often roles don’t need an advanced degree. Profiles are frequently beefed up unnecessarily and even the name of the university can trigger bias. How many times have you felt your spirits lifted by a candidate from a “good” university? Or been dismissive of either an obscure institution or one that has a bad reputation?
Years’ experience: Setting a limit to the number of years’ experience can help combat bias against ageism.
Post code: A post code can raise bias about a person’s socio-economic background.
Date of graduation: This leads to bias related to age at both ends of the spectrum.
Employments gaps: If these are no longer visible then it reduces bias against candidates who have taken parenting leave or any sort of gap period in their career.
Photos: In some geographies it is still common to supply photos. If these are masked then bias related to age, gender, physical appearance or ability and ethnicity is managed successfully.
Taking on unconscious biases
Pilot studies reveal that when using blind reviews, gender biases are reduced. This generates an equal number of male and female candidates. Companies working in the space offer competitive solutions to understand where is bias introduced in an interview process and to prioritize actions to manage bias more effectively.
This type of software which allows blind reviews is becoming commonplace in many large organisations. Eventually it will be standard in most HRIS systems. Without this external objectivity, reviewers (and therefore candidates) are at the mercy of their own unconscious biases.
What blind reviewing won’t do is tackle the biases than creep in before the sourcing stage of the process and later into the hiring procedure at interview or offer.