AI for Job Boards: What’s the Impact of Machine Learning on Recruitment?

While most technologies apply to specific industries, niches, and tasks, every once in a while, a technological invention will prove to be so disruptive that it permeates every aspect of our lives. Artificial intelligence is precisely that kind of invention — as it evolves, it will affect everything in our everyday lives, including the process of recruitment via job boards.

Here, we take a look at the impact of AI for job boards and what the implications are for associations like yours.

What are the implications of AI for job boards?

The most practical application of artificial intelligence has been machine learning.

It’s one of the many branches of AI, but one that’s far-reaching and opens up numerous possibilities. Machine learning uses advanced algorithms and massive data sets in an attempt to imitate how real humans learn. Basically, these data sets are “fed” to the algorithm, and the more information it processes, the more likely its predictions are to be true.

This current AI technology has huge implications for job boards and associations in general. But how has this technology changed job boards in practice?

Better job searching

When job applicants search for jobs, a machine learning algorithm can quickly help them arrive at the job ads they’re most qualified for through understanding their intent better. All the AI needs to accomplish this is access to some primary data about the applicant’s searches and their behaviors.

Similar to how machine learning has become integral to the relevancy of search results on Google, Bing, and other search engines, machine learning has become critical for job boards and jobseekers in the improvement of their user experience through receiving more relevant results.

Advancing Underlying Technology

With advancements in cloud technologies, the ability to use AI within job board solutions is more readily accessible. Storage of the large datasets required is now cost-effective, and businesses such as AWS are introducing technologies that allow scalable deployment of machine-learning models.

Companies are driving advanced innovations in job board solutions that are now seeing candidates being served more targeted, personal jobs that better align to their unique interests and career goals, improving click-through rates and overall response rates. With this rapid development of the underlying technology, candidates expectations are growing at that same rate. Each year, they expect a more personalized engagement from the boards that they use.

Improving the jobseeker experience

As competition to attract highly qualified candidates increases and matching technology advances, poor quality content risks talent becoming disenfranchised with your brand. The result is less traffic, higher unsubscribe rates and a decrease in employer advertising spend and non-dues revenue.

Optimizing the user experience of your site and the matching technology that powers it should be at the heart of your strategy. This should include:

Suggesting relevant jobs to candidates with unparalleled ease
Automatically connecting employers to qualified jobseekers
Re-engaging passive candidates with highly targeted opportunities

In all these ways and more, AI contributes greatly to making the jobseeker experience more seamless and relevant for candidates.

How will machine learning impact recruiting?

Machine learning can benefit job board owners and operators in a number of ways, including reaching candidates more effectively. Although this technology is still imperfect, it’s already had a major impact on recruitment and will have even more influence in the years ahead.

Reaching passive candidates

Plenty of recruiters and companies miss out on excellent candidates simply because the latter just aren’t looking for work at the right time. However, a vast number of them would probably be interested in hearing an offer that’s better than what they have at their current job. Hiring statistics show that 81% of employees would consider leaving their current role for the right offer, even if they’re not currently looking for a new job.

These types of candidates, known as passive candidates, are not considered active jobseekers but are still receptive to learning about new opportunities and are motivated by certain triggers, such as a specific company, a desirable salary range, or a favorable job location.

Typically, they’re more selective about their jobseeking sources and use alerts, but usually don’t actively search for new jobs. Essentially, these candidates are opportunistic — they may be passive jobseekers, but they will jump at the right opportunity if presented with it.

Natural Language Processing (NLP), an area of machine learning, can be a big help here — allowing hiring managers to find such passive candidates and present them with the opportunities that address their particular pain points.

This NLP technology can be used to extract data points out of resumes, as well as translate that data into various groupings based on common themes. NLP can also be used to identify job titles and create canonical versions of them for comparison, empowering jobseekers with more information about roles. Overall, it has a number of key uses for job board owners and operators.

Minimizing bias and discrimination

While the recruitment process has made significant strides in eliminating bias and discrimination to improve diversity, AI for job boards and machine learning tools can be used to refine the procedure further — although challenges remain.

Even though eliminating bias has long been one of the ultimate goals of AI and machine learning, the reality is that more work needs to be done in this area. If you’re mass-filtering through data sets, it’s still important to be aware of bias and plan on how to manage it, as it’s one of the largest threats to using AI for HR purposes.

Therefore, it’s essential to make sure that the procedure for AI-based assessments receives proper design and testing first — otherwise, it could develop some accidental bias as well. Treading carefully and realizing that the technology is still imperfect is the key here. Madgex, notably, deals with one of the largest online recruitment data sets in the industry and has extensive experience with rigorous testing.

Understand AI for job boards to improve your association’s career center

Although AI in job boards is a relatively young technology, it’s already had a positive impact.

Like all machine learning technologies, AI-driven job boards and application processing are getting better, more effective and more creative over time. This gives human recruiters some much-needed space to focus on the human side of their top applicants and gives them enough time to make better-informed decisions on hiring.

Could AI free up so much of recruiters’ time that it finally gives them enough bandwidth to get back to all their potential candidates? Quite possibly!

Ultimately, AI for job boards can lead to increased revenue and greater audience engagement for associations like yours.

By better understanding the data that flows through your career center, your association can create more targeted marketing campaigns. By leveraging this data for their career centers, associations can give their candidate base a much smoother, more personalized experience that can draw back passive jobseekers time after time.

AI for job boards: What’s the impact of machine learning on recruitment?