Business Job interview. HR and resume of applicant on table.

Career-changing decisions empowered by AI tool

Image credit: Sirinarth Mekvorawuth/Dreamstime

Researchers in Australia have developed an artificial intelligence (AI) method that can identify and recommend jobs to workers looking for a new role.

The tool, created by researchers from the University of Technology Sydney (UTS) and UNSW Sydney, uses machine learning to identify and recommend jobs with similar underlying skill sets to a person’s current occupation. The system can also respond in real-time to changes in job demand and provide recommendations for the precise skills needed to transition to a new occupation.

According to Dr Nikolas Dawson from the UTS Data Science Institute, while workplace change is inevitable, if experts can make the job transition process easier and more efficient there are significant productivity and equity benefits not only for individuals but also for businesses and government.

“It can be a daunting proposition to switch to a new career, particularly for those who have been in the same job for a long time,” he explained. “Successful transitions typically involve workers leveraging their existing skills, and gaining new skills, to meet the demands of the new occupation.”

Professor Mary-Anne Williams, the chair in Innovation at UNSW Business School, said that the new recommender system can help to reduce the inevitable stress during times of job loss by lowering the costs of job transitions and providing evidenced-based recommendations that better meet the needs of individuals with specific skill sets that often transcend their occupation.

“By focusing on skill sets, rather than occupations, this new approach helps workers, organisations and businesses like retraining advisory services discover the new skills a person would need to gain to get a new in-demand job and assess the associated training investment required,” she said. “In addition, organisations can use our skill similarity measure to design completely new or hybrid occupations that increase the likelihood of finding people with the skill set.”

Dr Marian-Andrei Rizoiu from the UTS Data Science Institute added: “If we can move towards skills-based hiring, rather than defining an occupation by its job title, then we can help people identify the specific skills they have, or need to develop, in order to find productive and meaningful work.”

The researchers used valuable data from Burning Glass Technologies, an analytics software company that provides real-time information on jobs and labour market trends, to examine and parse the underlying skill sets of over eight million jobs advertised in Australia between 2012 and 2020. They then compared the job transition predictions with data from the Household, Income and Labour Dynamics in Australia (HILDA) survey, which tracks participants over the course of their lives, to validate these predictions with nearly 3,000 real-life examples.

According to the researchers, the jobs recommender system accurately predicted job transition probabilities and could also show whether it is easier to move in one direction than another. They added that the method could be leveraged by educators, government, and business, potentially with data from the Australian Bureau of Statistics, to support industries and sectors undergoing significant upheaval to transition workers at scale.

As part of the study, the researchers also built an early warning indicator of emerging technologies (such as AI) that have the potential to disrupt labour markets. This information could allow policymakers and businesses to better prepare for future structural shifts, they explained.

“If you look back in history, it’s almost never the case that there are fewer jobs because of automation, but new jobs are created at the same time old ones disappear. So it is fundamental that people can build the requisite skills and transition smoothly into these new jobs,” Dawson said. “The ability to undertake micro-credentials in specific skill areas, customised for the individual, will likely be a key part of this future.”

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