Amid the dangers and health risks associated with the COVID-19 pandemic, businesses had to reshape several processes to adapt accordingly. The hiring process, for example, has become more dependent on remote interviews and digital platforms.
This shift was a watershed moment that ushered in a flood of new technology, platforms, and tools that were previously, at best, on the fringe of conventional hiring. The use of automated platforms powered by artificial intelligence or AI, in particular, quickly gained a lot of popularity in recruitment circles.
While there is no denying the cost-saving and minimal-downtime benefits that AI automation offers, it is not the all-powerful technology it is often portrayed up to be. At least not yet. While recent advancements in processing, computing, and data science have indeed enabled the creation of computers that can work independently, the use cases are far more limited than the general perception.
For recruiters, understanding the distinction between the AI hype and its real-world applications is extremely critical, particularly when sourcing technical resources. Read on to discover more about AI in tech:
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AI Use Cases in Tech Hiring and The Associated Dangers
For many business functions, automation is by far the most appealing aspect of modern AI technology. The recruiting function is no different. AI can streamline the review and applicant management process.
It can screen, shortlist, and even communicate (on a limited scale) with candidates. It can also help to minimize the usual human errors that can crop up, such as in high-volume hiring drives, regardless of centralized vs decentralized recruitment models.
AI can perform most takes independent of a human recruiter’s involvement; at the very least, it requires minimal supervision. As a result, many in-house recruiters now depend heavily on AI-powered applicant management systems, particularly for niche or specialized hiring scenarios and technical roles.
However, there are some aspects of AI that may undermine the overall hiring goal: onboarding high-quality and valuable talent. Three of the most significant dangers associated with over-dependence on AI problems in tech hiring include:
Programming Bias
Biased AI may sound counterintuitive, but the knowledge that it exists is fairly common. How does this happen? After all, a machine is inherently incapable of being biased or creating a toxic work environment.
The programmer or developer involved in an AI model’s creation, however, is extremely susceptible to programming bias. Moreover, the existence of unconscious bias further increases the risk. This can creep into both the programming and the datasets that the AI initially trains on before rolling out for a market release.
When trained on datasets that underrepresent certain genders, ethnicities, or races, the AI “learns” bias in its formative training. As machine learning is a crucial part of AI functioning, the bias can enmesh itself deeply within the AI’s programming. While the technology itself may not have the capacity to be biased towards specific demographics, the programming is susceptible to it, even early on.
The business impact? Losing out on valuable tech talent simply because their respective demographic had little to no presence in the AIs development scheme. In the long term, this leads to a more homogenous workforce, a drawback to any evolving business.
Limited to Structured Data
AI, despite its significant computing power and ability, still works best within structured environments. It may reject or incorrectly assess datasets that are not structured accordingly. In recruiting terms, it may not be capable of “reading between the lines”. As a result, tech applicant management systems could be over–screening incoming applications.
However, even unstructured applications don’t preclude the applicant’s potential value and/or potential to bring a high degree of expertise, innovation, and productivity to a workplace. This simple limitation could potentially be screening out world-class tech applicants before recruiters can even have a look at their resumes and spot them for who they are.
Difficulty in Identifying Bias
The most significant problem with AI is the same as its biggest strength: complexity. Most recruiters are not trained as data scientists or developers capable of spotting problems with code integrity. Moreover, most AI platforms follow the SaaS model in being off-the-shelf solutions.
Recruiters, even in-house developers, may simply not have access to examine the AI’s programming. As a result, there is a high probability of programming bias or data structure limitations remaining undetected.
Human Involvement is the Only Real Solution in AI Tech Hiring
Unless recruiters have a greater familiarity with data science and/or coding, the only sure way to mitigate the risks of AI impacting hiring success is to avoid becoming overly reliant on it. In other words, it is still too early to allow AI to work completely independently of human involvement/intervention.
There is no understating the potential of AI. But, despite all the hype, the technology is very new, and therefore still in its infancy. The problems associated with it are likely to continue until it evolves beyond them.
Therefore, recruiters still need to play a key role alongside AI in managing applicants, especially for technical or specialized staffing needs. In particular, human involvement is essential to mitigate screening bias, vetting candidates, and isolating false positives/negatives. It may prove far more helpful for businesses to diversify their hiring approaches.
The judicious use of AI, in-house recruiters, and third parties may prove the most sensible and low-risk. Pittsburgh staffing firms like CGT Staffing with demonstrable expertise in tech and IT staffing is far more likely to deliver better hiring success.
The alternatives, of course, are to simply miss out on great potential hires, decrease the chances of hiring success, or onboard an increasingly homogenous tech workforce that will ultimately threaten innovation and sustainability.
