Fairest of All

Dear Impossible Readers,

This is the first post in The Καιρός Principle series.

The other day, my friend visited me and told me she applied for a job on a website. They asked her to remove her profile photo so no one could see what she looks like, to make the process fairer. Based on her name, it was still obvious that she is of northern European origin and a woman. In this case, the first should work in her favour, and the second, not so much. So, how could we make job applications fairer?

One approach is to anonymise candidate information during the application process. Instead of seeing a photograph, name, date of birth, nationality, disabilities, or visa requirements, recruiters would only see a random serial number alongside relevant qualifications, skills, and experiences. Any potentially biased information would be concealed until candidates reach the interview stage. At that point, full profiles could be revealed, providing companies with the necessary information for the interviews. This approach bypasses recruiter perceptions, enforcing fairness by ensuring shortlisting is based solely on qualifications. Research indicates that anonymising shortlisting can increase the chances of women and underrepresented groups being fairly considered.

Building such a system is simple in principle. Each candidate could be given a random identifier to replace personal information, while the original data remains securely stored. The platform interface would only display anonymised information for shortlisting. Companies could implement this either as a browser plugin or an integrated component on the job platform, enabling easy adoption without significant changes to existing systems. Ensuring compliance with privacy regulations such as GDPR would be crucial, but the technical challenge remains manageable.

Challenges persist. Adoption relies on companies being willing to employ the system, and integration across various platforms may necessitate multiple strategies. Legal and privacy safeguards must be meticulously implemented. Future enhancements could include anonymising subjective assessments or connecting the system with AI-driven shortlisting tools, removing sources of bias. Even in its most basic form, anonymised shortlisting could foster a fairer, more inclusive hiring process, enabling candidates to be assessed mainly on their qualifications.

Who is the fairest of them all?
Yours Possibly