People looking to change careers could get help from a machine learning-based method, which, instead of focusing on jobs, looks to identify a skills match between different occupations.
Researchers from the University of Technology Sydney (UTS) and the University of New South Wales (UNSW) have developed an AI tool that can identify and recommend jobs with similar underlying skill sets to someone’s current occupation.
Dr Nikolas Dawson and Dr Marian-Andrei Rizoiu from the UTS Data Science Institute and Professor Mary-Anne Williams from the UNSW Business School, developed the system based on findings from a new study on skill-driven job recommendations, published in the international journal Plos One.
This new approach can enable workers, organisations and businesses such as retraining advisory services to discover the new skills someone would need to acquire to obtain a new in-demand job and assess the associated training investment required.
“It could be broadly applied within workplaces, universities for students, and for individuals via a consumer application,” said Dawson, who is currently working on a similar career transitions application at FutureFit AI.
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The system can respond in real-time to changes in job demand and provide recommendations of the precise skills needed to transition to a new occupation.
The researchers hope that the platform can support people undertake the difficult and sometimes distressing challenge of finding a new occupation quickly due to technological and economic change, or crises such as the COVID-19 pandemic.
Dawson said while workplace change is inevitable, helping to make the job transition process easier and more efficient to provide significant productivity as well as 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.
“Successful transitions typically involve workers leveraging their existing skills, and acquiring new skills, to meet the demands of the new occupation,” he said.
The researchers used valuable data from Burning Glass Technologies, an analytics company that provides real-time information on jobs and labour market trends, to examine and parse the underlying skill sets of more than 8 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.
The jobs recommender system accurately predicted job transition probabilities and was also able to show whether it is easier to move in one direction than another.
The system works marginally better with technical and healthcare-related occupations, according to Dawson.
“This is because we developed our system using skills data from job advertisements where there's a greater exposure to these types of occupations,” he told Information Age.
“However, given the coverage of the underlying dataset, the skill and job recommendations are still robust across all occupations,” he added.
The researchers expect the methods developed in the study could be used 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 artificial intelligence) that have the potential to disrupt labour markets.
This information could allow policymakers and businesses to better prepare for future structural shifts.
Future areas of innovation will be in providing highly localised labour market information.
“It can inform recommendations, incorporating variables beyond skills, such as interests and personality profiles, and better identifying precisely when and what types of education should be recommended,” Dawson said.