How a start up created a digital recruiter to diversify the techies background.

Silicon Valley shows an archaic stereotype of programmers: young men from top Ivy League schools and who are often hired by their friends or former fraternity in the technology industry. Essentially, your pedigree is worth more than your talent.
As a result, there is a severe lack of diversity as those who are succeeding in tech industries have almost identical background.
Some claims new types of meritocracy should appear — AI based. As a result, machine learning algorithms should make promotion decisions, internal versus external hire, business units reorgs, hiring tickets approval etc.
This is the aim of Gradberry, a Y combinator start up that has created an artificial intelligence capable of recruiting the best candidate for tech companies, based on skill. This Artificial Intelligence is called TARA (Talent Acquisition and Recruiting Automation). The measure is based on analysing the code workers have already written. Any biographical information such as name, gender, age, past works… is set apart. TARA grades programmers on a scale of 1-10. Nobody can attain a 10 but currently the highest score belongs to a 9 years old boy!
This project recruits and manages the best programmers for a various range of projects for business, from creating a website to complex and advanced applications.
This AI enables the recruitment of highly qualified people and increased the chance for women (huge lack) to have a job or minorities who have historically been lacking from cutting-edge start-ups and tech companies.
TARA is an example of AI which is about to change the way of recruiting by diversifying the pool of knowledge from various backgrounds in tech companies and more generally in the digital world.
Creating a meritocracy may no longer be a pipe dream, some would enlarge it to other recruiting fields. Before this next step, AI will have to analyse objective criteria which is a difficult challenge. Until this, raising awareness among recruiters may be the more efficient way to reduce employment inequalities and targeting the meritocracy.