Researchers develop a new AI-powered tool to identify and recommend jobs


Date:
August 5, 2021
Source:
University of Technology Sydney
Summary:
Researchers have developed a machine learning-based method that
can identify and recommend jobs to workers looking for a new role.



FULL STORY ==========================================================================
Car manufacturing workers, long haul airline pilots, coal workers, shop assistants -- many employees are forced to 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.


==========================================================================
To make the job transition process easier, and increase the chances of
success, researchers from the University of Technology Sydney (UTS)
and UNSW Sydney have developed a machine learning-based method that
can identify and recommend jobs with similar underlying skill sets to
someone's current occupation.

The system can also respond in real-time to changes in job demand and
provide recommendations of the precise skills needed to transition to
a new occupation.

Developed by Dr Nikolas Dawson and Dr Marian-Andrei Rizoiu from the UTS
Data Science Institute and Professor Mary-Anne Williams, the Michael J
Crouch Chair in Innovation at UNSW Business School, the system is based
on findings from their new study, "Skill-driven Recommendations for Job Transition Pathways," published in the international journal PLOS ONE.

What are the benefits of using AI to find a job? Dr Dawson says while workplace change is inevitable, if we 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. Successful
transitions typically involve workers leveraging their existing skills,
and acquiring new skills, to meet the demands of the new occupation,"
he said.

Professor Williams says 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 met 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 acquire to
obtain 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 necessary skill set.

"In the current rapidly changing job market the need to continuously
upskill is a challenge for individuals and organisations. Our recommender system can help individuals embrace change by proactively designing
their lifelong learning journey and to react to new more exciting
job opportunities as they arise by determining the next best skill to
acquire." Dr Rizoiu 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."


==========================================================================
How was the job-search method created? 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 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 3000 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 methods developed in the study can 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 artificial intelligence)
that have the potential to disrupt labour markets. This information
could allow policymakers and businesses to better prepare for future
structural shifts.

Dr Dawson undertook the study as part of his PhD in computational
economics at UTS with Professor Williams and Dr Rizoiu. He now works as
a senior data scientist at FutureFit AI, a company that partners with
industry and government to provide an AI-powered tool to help workers
navigate career transitions.

"If you look back in history, it's almost never the case that there
are fewer jobs due to automation, but rather new jobs are created at
the same time old ones disappear. So it is fundamental that people have
the ability to build the requisite skills and transition smoothly into
these new jobs," Dr 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." ========================================================================== Story Source: Materials provided by University_of_Technology_Sydney. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Nikolas Dawson, Mary-Anne Williams, Marian-Andrei
Rizoiu. Skill-driven
recommendations for job transition pathways. PLOS ONE, 2021; 16
(8): e0254722 DOI: 10.1371/journal.pone.0254722 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210805124607.htm

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