Gloat’s AI driven career finding platform compares a user’s skill set and career history with millions of others to provide data driven recommendations for smart career moves. To understand how such a system works in practice, Digital Journal spoke with Ben Reuveni, CEO and Co-Founder of Gloat and its InnerMobility Platform.
Digital Journal: What are the main factors affecting recruitment for businesses?
Ben Reuveni: There are a number of elements affecting today’s recruitment process, most of which fall into two main categories of external and internal factors. External recruitment factors are broader- things like a country’s socio-economic conditions, the hiring market’s supply and demand, and national employment rates. These factors put a strain on businesses that they may not be able to control. Internal factors are often specific to the company- things like the company size, the organizational culture, and the company’s growth rate. Naturally, these internal factors are easier to control and manage.
Zoom in on a company’s human resource department and many recruitment issues emerge. The tedious process of sifting through resumes, the attempt at impartiality to battle unconscious hiring bias, and the occurrence of genuine human error are frequent flaws in the recruitment process. Mistakes in the hiring process are extremely costly, in fact according to the U.S. Department of Labor, the price of a bad hire is at least 30 percent of the employee’s first-year earnings.
One of the largest external factors presently affecting recruitment for businesses is the changing makeup of the workforce. Millennials are already the largest generation represented in the U.S. workforce and 61 million Gen Z’s are set to join their slightly older colleagues. The 2018 Deloitte Millennial Survey, found that 43 percent of millennials and 61 percent of Gen Z’s expect to leave their current jobs within two years. This is placing an immense strain on recruitment for businesses, as well as encouraging companies to look at new ways to improve employee retention.
DJ: Is most recruitment now online?
Reuveni: Given the change in population demographics, most recruitment is now done online. This can come in the form of direct interaction through a company’s website, the utilization of a third-party job board, or a recruitment agency. As company’s recruitment practices continue to move into the digital space, AI becomes more significant.
In 2018, 15 percent of HR leaders in 40 countries shared that they believe AI is already impacting the workplace, and an additional 40 percent believe that AI will significantly influence their decision making in the coming two to five years, including hiring decisions. While that first number might seem small, taking into account the more traditional nature of the human resources profession across all industries, this is quite significant.
DJ: How can artificial intelligence assist with recruitment?
Reuveni: Artificial intelligence can drastically improve the hiring process for both a company looking to make hires and for the candidates themselves. From the company perspective, AI can process tasks at a scale that most HR teams would struggle with, substantially increasing the efficiency and accuracy of the hiring process. By combing through countless amounts of resumes in seconds, contrasting them with others and presenting the best candidates of the bunch to the hiring manager, HR professionals are able to focus their time on interviewing the best candidates available and improving other areas of the workplace.
AI can also be utilized to dive back into a company’s existing pipeline of potential talent, as 65 percent of resumes for high-volume roles are completely ignored. Most companies would be shocked at the amount of quality talent that’s been built up in their databases over time. AI can also act as an impartial judge of information and help to ensure that specific hiring biases like age and gender get removed from the hiring equation all together.
DJ: Is there any risk of bias with AI?
Reuveni: There is always a risk of AI developing some sort of bias. AI systems only learn what a human being teaches it, which therefore exposes AI platforms to having human error ingrained in the system. The premise is simple, what goes in affects what comes out. As AI is totally dependent on the data it utilizes, if the data is limited in scope or tainted in anyway, bias is a possible outcome.
A recent example of this occurring in the recruitment sector can be seen with Amazon. Amazon employed AI to enhance their recruitment method, however their system was inherently biased against women. During the initial learning phase when Amazon employees were creating the AI code, the recruitment platform was fed resumes of mostly men to compare and contrast to look for patterns amongst successful hire. The result was the AI platform unintentionally discriminating against female candidates.
The potential for AI bias is real, and it’s important that companies harnessing AI recognize the problem and take steps to prevent AI hardcoding existing biases.
