We’re not quite yet on the cusp of having robots for bosses. But we’re definitely entering the age of algorithm-driven AIs doing all the hiring… which may not be a good thing. According to recent surveys, more than half of all HR managers expect hiring algorithms to be a key part of their future. Increasingly, artificial intelligence (AI) and machine learning (ML) are being leveraged to help firms recruit and retain employees. The assumption is that such systems can eliminate human bias and improve the hiring process efficiency. But there’s evidence that AI hiring may not be any better than current HR practices.
Dozens of major corporations already use ML and AI hiring approaches. Likewise, several new companies and startups have introduced hiring algorithms that are receiving a great deal of attention. Certain industries in particular hope that hiring algorithms can solve their current dilemmas regarding employee vacancies. But the cost of using ML and AI hiring programs must be weighed against their benefits. And there might be a need for refinements before such systems can be best utilized.
Hiring Algorithms: How Companies Utilize Them Today
Major corporations are already using ML and AI hiring systems to help with their HR department strategies. For example, Hilton and Goldman Sachs have both employed such systems as have many other major companies. There may be an assumption that hiring algorithms will be for resume review only. However, this is far from the truth. In fact, the involvement of ML and AI hiring systems cover job advertising, resume ranking, and late-stage employee selection. While HR personnel still make the final decisions, hiring algorithms are playing an increasing role elsewhere.
As you might expect, several companies are promoting ML and AI hiring systems to major corporations. CEO Mike Rosenbaum leads Pegged Software. This company facilitates employee recruitment and hiring for healthcare as long as it doesn’t involve executive recruitment. Another notable hiring algorithm company is Incredible Health, a startup out of San Francisco. This company recently received $15 million in Series A funding for its nursing hiring platform development. These are two of dozens of such companies now entering the ML and AI hiring application field.
Do Hiring Algorithms Really Improve Hiring Quality and Reduce Bias?
In theory, there’s a belief that the use of ML and AI hiring systems may eliminate human biases that may exist in the hiring process. When HR personnel recruit, interview, assess and make hiring decisions, inherent personal biases can exist. But by using objective data, the assumption has been that hiring algorithms erase this potential. The end result should, therefore, be more fair and accurate determinations about hires. While this seems logical, in actuality this is not the case. Research examining ML and AI hiring systems has shown that these types of systems have biases of their own.
In this regard, hiring algorithms can base decisions on existing institutional and historical patterns of bias. Amazon found this out recently when its ML and AI hiring program was found to show a preference for male tech hires. Based on 10 years of company data, the hiring algorithm presumed male applicants were preferred based on their past predominance. Similar studies of ML and AI hiring systems have shown tendencies for both gender and racial biases. In one study, 85 percent of those picked by a hiring algorithm for a cashier position were female. In another, 75 percent chosen for a taxicab driver job were African American.
Key Industries in Need of Hiring Algorithm Solutions
Understandably, many industries could benefit from effective ML and AI hiring systems if key issues could be resolved. But some sectors have a greater need for effective hiring algorithms than others. Healthcare specifically has a tremendous need for more effective hiring solutions given the increasing demand for its services. With 80 percent of older adults having at least one chronic disease, staffing needs are rising rapidly.
In this regard, nursing positions reflect an area where there is a need for urgent assistance. Healthcare systems routinely have challenges with high nursing turnover, poor retention, and early retirement. By 2024, the estimation is that there will be a need for roughly one million RNs throughout the nation to fill employment vacancies. Organizations are competing for hires given that the unemployment rate for nurse practitioners is around one percent. Likewise, skills gaps are substantial for the sector. Such institutions would greatly welcome ML and AI hiring systems that improve this process.
ML and AI Hiring Systems – A Work in Progress
While it is evident that there’s a need for many improvements, some benefits have been noted. For example, some healthcare systems have seen both employee turnover reduce and quality of performance improve with hiring algorithms. These results are promising and support continued development of these ML and AI hiring systems. But for now, close oversight of these hiring algorithms will be needed. The legal risks that could develop if biased and discriminatory practices occurred could be profound. But with time and further refinement, those in the industry anticipate such problems will be effectively addressed. And for specific industries like healthcare, these developments need to be sooner rather than later.
