Genomawork uses AI to predict job seekers’ potential

From small businesses to multilateral organizations, achieving holistic, sustainable, and inclusive digital transformation is at the top of economic strategies. One sector that is both undergoing and driving change is human resources (HR). According to Brian Kropp, Group Vice President at Gartner, “Digitalization is changing everything; from core functions like the way we hire and develop talent to new burdens such as raising performance.” 

In this context, the stability and prosperity of organizations hinge on HR’s ability to find the right person. This entails not only recruiting someone with the right skills for the right job, but doing so at the right time and for the right cost. And these different moving parts will only grow more constraining as recruitment practices and professional opportunities are affected by the economic challenges created by the COVID-19 crisis.

Chilean HR start-up Genomawork is looking to help organizations avoid such struggles by leveraging AI to make recruitment processes more efficient, predictable, and successful.

Finding the right fit with AI

Genomawork uses Genoma, an AI algorithm that detects candidates’ personality traits and cognitive skills and assesses them against a set of pre-defined reference performance indicators.

A company only has to define the ideal traits or skills it is looking for in a given position, and Genoma identifies which potential candidate is the right cognitive and/or emotional fit. It does so by processing thousands of inter-linked variables collected when candidates play a series of online games developed specifically to assess their choices, actions, and reactions.

These neuroscience-based games allow the AI to pick up on a multitude of personality traits, providing unprecedented insights on candidates’ behavior and personality. It also predicts how the candidate may execute his or her duties once on the job, as it can compare his or her traits with those of an ideal candidate defined as a reference model

An objective and precise automated recruitment process
An objective and precise automated recruitment process
Source: Genomawork

Nothing left to chance

Genomawork complements this automated process with more traditional face-to-face video interviews that further indicate candidates’ viability in terms of language proficiency, cultural fit, and system of values – many elements that cannot yet be captured via the gaming exercises.

Once the process is completed, Genomawork gives companies a ranked assessment of applicants, including information on people that may lack relevant experience but possess hidden potential for a given position.

This benefits companies by allowing them to make the final call with all the necessary elements in hand, including things they might not have suspected they needed to look for. This not only cuts down the time and effort spent sifting through resumes, it ensures that all bases have been covered before a decision is made.

“Genomawork has had an 85% accuracy rate in matching candidates with a company’s recruiting needs,” says Daniel La Roche, Co-Founder of Genomawork. “Responses have been very positive in qualitative terms, and we expect them to remain that way as our clients begin measuring the performance of employees who were hired through Genomawork.”

For candidates, the benefits are much more introspective; they can access their assessment and find a trove of information they may not have known about themselves – the way they think, the way they approach problems, how they perform, under pressure, etc. 

“On a scale of one to ten, job applicants give Genomawork an average score of 9.2,” says La Roche. “This shows that their overall experience with the platform is positive, even when they were not selected for the job they applied for.”

Assessment of candidates’ trait and skills against a base reference model
Assessment of candidates’ trait and skills against a base reference model
Source: Genomawork

Removing biases from the recruitment process

For La Roche, using AI for the purpose of recruiting is much more than simply deconstructing candidates’ personalities and experiences into algorithmic variables. And, even though it may be a key objective for many of the company’s corporate clients, it is not only about increasing accuracy and efficiency.

Indeed, the Genoma system also aims to make recruitment fairer and less biased – a particularly important endeavor in a world where most organizations still rely on inconsistent and indiscernible methods to recruit the people they want to invest in.

Despite its many limitations – over-reliance on cliché words and expressions, unverifiable or exaggerated information, physical limitations and constraints of the paper format, to name but a few – the curriculum vitae (CV) remains the main vector of assessment of an applicant’s potential fit.

Over the years, the process of submitting a CV has been made slightly less arbitrary by making it unnecessary to include identifiable information that may be used against an applicant (photo, name, gender, address, date of birth, marital status), but it remains an unreliable predictor of professional ability and performance.

“CV-based recruitment is an inaccurate and costly process for companies,” says La Roche. “The CV is a bad predictor of performance and there is a high probability of making a judgement error and re-starting the recruitment from scratch after a few months. The direct cost of this error can be significant, as well as the cost of missing a potentially promising candidate just because there were too many CVs to read.”

In addition, there are many biases at play, biases that even the people in charge of selecting and interviewing candidates may not be aware of.

“From the moment a CV falls into your hands, a thousand micro-judgement calls are made, most of which are heavily biased,” says La Roche. “A person’s name, the university they attended, the extent of their social and professional networks – all of these elements can work for or against a person, regardless of their relevance to the job and regardless of their impact in the person’s skills or ability to perform.”

In this sense, Genoma is not just an algorithm that automates functions and saves time and money. It is also a disintermediation tool that removes the limitations created by a subjective and biased assessment of people’s true potential.