How machine learning can help alleviate the labor shortage in the United States

Check all sessions on demand from Summit Smart Security here.

Experts were discussing the reasons shortage of workers In the United States, however, one thing is painfully clear: There is a staggering disparity between the number of jobs available (More than 10 million) and the number of workers looking for work (about 6 million).

In this short article, we’ll step back and look at how we got here, the multiple factors that have led to such a disparity, and some of the solutions that are being implemented to try and combat this problem. Significantly, we’ll take a look at machine learning (ML) and how it is used to mitigate the causes and effects of labor shortages in the United States

The current labor shortage in the United States

according to american chamber of commerce, The labor force participation rate has decreased in recent years, dropping from 63.3% to 62.3%. While a 1% drop in the number of able-bodied workers participating in the workforce may not be a huge problem nationwide, it comes after a pandemic that has left more than 30 million workers losing their jobs.

The industries hardest hit include entertainment, hospitality, food services, durable goods manufacturing, education, and health services. However, there is no sector of activity that has not been affected.

It happened

The pinnacle of smart security on demand

Learn about the critical role of AI and machine learning in cybersecurity and industry-specific case studies. Watch sessions on demand today.

Watch here

What are some causes of labor shortage?

The COVID-19 pandemic has already shaken the job market. studies Watch That about a quarter of a million working-age people have died from the disease, half a million have left the workforce due to the ongoing health effects of the virus, and a similar number of workers have moved directly from illness into retirement.

This decline in the workforce should have been compensated for by job seekers looking to enter the market, but this has not happened. Instead, the US saw its monthly quit rate rise across all sectors. In some industries, such as entertainment and hospitality, the monthly Smoking cessation rate exceeds 6%. The more traditionally stable sectors, such as business and professional services, still post an alarming take-off rate of more than 3%.

Many workers have expressed their desire to continue working from home. This is an expectation that is difficult to meet for some industries, such as health services and manufacturing. But this shift in employee expectations Just scratch the surface. On-the-job childcare, a shorter workweek, better work-life balance, and ongoing training top the list of what employees demand of employers, and companies are slow to catch up and adapt to the change in employee-employer dynamics. This partly explains why, even though the nationwide hiring rate is much higher than normal, companies across all sectors remain with millions of jobs yet to be filled.

What is machine learning?

Although often used interchangeably with AI (Artificial intelligence), ML is specifically a subset or application of AI. In simple terms, ML is the application of big data where machines (computers) use mathematical models to develop new understanding without explicit instructions.

For example, Image recognition It is a widely used application of ML. Through image recognition, computers are able to recognize and match faces (tagging social media posts) or identify cancerous tumors in X-rays.

ML is also widely used in the financial sector in what is known as statistical arbitrage: the use of algorithms to analyze securities in relation to specific economic variables.

ML also allows computers to examine large data sets, identify causes and correlations, and extrapolate from their predictions and probabilities. Predictive insights help you get the most out of your data. Applications of this predictive ability are found in real estate pricing, product development, and other fields. Predictive analytics It can also help job seekers and recruiters find better matches than they have been finding so far.

How does machine learning help solve the US labor shortage?

The current labor shortage in the United States coupled with an alarmingly high quit rate has shown that there is a problem: workers are having trouble finding jobs that suit them.

Recruiters and job seekers are increasingly turning to advanced algorithms and statistical analysis of big data to help mitigate this problem.

ML has the ability to analyze large sets of data—in this case, workers who have quit or been relieved of their duties versus those who have staying power or who have been promoted—and identify common traits, characteristics, and skills. With this understanding, recruiters can filter out candidates who are not likely to be successful in the position for which they are applying faster and more accurately. The result is a faster and smoother job search that is more likely to yield positive results.

In addition to improving the matching process, ML has a positive effect on the speed and duration of the recruitment process. The extremely long time a job seeker spends applying for and then interviewing for a job for a job they are not likely to get or be happy with can only aggravate the job seeker’s situation. When faced with a vacancy crisis and high quitting rate, we need job seekers who are passionate about the recruitment process and not frustrated with it.

Evolution of the online job portal

Traditionally, it was an online job portal where job seekers could view available jobs in their location or sector of activity, read various descriptions and requirements, and then take steps to apply for jobs. While this is still a staple of online job portals today, the most successful sites are taking things a few steps further.

When you upload a resume to an online job portal that uses ML, the job seeker can be guided and directed towards jobs that best fit their skills and experience.

However, machine learning can do more than that. Having the required skills and experience is not enough to guarantee that the available job will be a good fit. We must take into consideration the job seeker’s personality and priorities. ML can also do this. By having the job seeker fill out a questionnaire, take a personality test, or complete problem-solving tests that incorporate motivation, an online job portal that uses machine learning gains insight into how the job seeker thinks and what kind of company or position they are likely to be. to be successful in.

Something small

In the United States, there are millions more jobs than people looking for work. The high employment rate can hardly keep up with the staggering rate of workers leaving their jobs. Thanks to advances in machine learning, computers can analyze large sets of data to identify infections and correlations that can help recruiters and job seekers find matches that are more likely to be successful in the short and long term.

Gergo Vari is the founder and CEO of Lensa, Inc.


Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people who do data work, can share data-related insights and innovations.

If you want to read about cutting-edge ideas, updated information, best practices, and the future of data and data technology, join us at DataDecisionMakers.

You can even think Contribute an article Your own!

Read more from DataDecisionMakers

Leave a Reply

Your email address will not be published. Required fields are marked *