Auntie Sally's Story
Auntie Sally is a union member at an electronics factory in Singapore producing wire bonds, the tiny wires in your mobile phone, tablet or step tracker that connect the semiconductor chip to its housing.
Underlying this trend is a deep, visceral and very real fear that the diligent, dutiful employee has of losing his job.
There have been many involved government policy discussion1 on how rapid technological disruptions and the progress of artificial intelligence will inevitably displace workers like Auntie Sally along the entire value chain,2 hollowing out the lower and middle classes. The pace of disruptive technologies makes it ever more difficult to train workers fast enough to transition to new jobs and sectors.
Can technology be used to tackle some of the very problems arising from its development?
Underlying this trend is a deep, visceral and very real fear that the diligent, dutiful employee has of losing his job. In part, this anxiety is reflected in global events, from the US elections and Brexit in 2016 to worldwide protests against Uber. The sheer potential impact of disruptive technology, adding to prevailing resentments about offshoring, could further deepen divides: between proponents who are able to extract benefit from it, and those who fear they may lose out.
There is plenty of literature on how we can harness technology to improve productivity in ways that make jobs easier, but risk displacing workers. The question is: In a technologically-enabled nation, can technology be used to tackle some of the very problems arising from its development? Could workers be involved not as mere spectators or recipients of help, but active players with a call to action, who can see for themselves the reality of a better, more hopeful life, who can make use technology to their advantage? Surely, workers would be more invested in the adoption of emerging technology if they can see themselves jointly sharing in progress. How can technology play a role in mitigating job displacements and making workers more valuable? I suggest three approaches.
HELPING COMPANIES IDENTIFY THEIR SKILLS NEEDS
The faster technology develops, the more quickly workplace skills needed to stay ahead will evolve. This biggest challenge for the labour movement or HR professionals is how to keep up.
When speaking with companies, what surprises me is that they too often feel around in the dark for skills they will need in the future, before suddenly realising that they are already behind—leaving too short a runway to train workers. For instance, data science was a skill which suddenly came into demand, prompting companies to scramble to develop expertise. But for some time, many did not truly understand how data science could add real value in specific contexts, nor what specific skills were required: so job descriptions for data scientists had grandiose expectations—asking for “unicorns”—that were almost impossible to fill.
Technology has a role to play in helping both government and companies, especially smaller ones, better anticipate the changing skillsets needed to remain competitive. Data mining and analysis can trawl through thousands of online job descriptions to fish out skills that are trending in particular sectors, or even identify emerging skills across sectors which may give rise to new niche areas of growth. Once identified, companies are better able to start training workers as soon as possible—before their old skills become outmoded.
Technology has a role to play in helping both government and companies better anticipate the changing skillsets needed to remain competitive.
MATCHING SKILLS, ELIMINATING BIASES
Just as Tinder and Coffee Meets Bagel help people find their other halves based on users’ preferences and profiles for a better fit, finding good matches between skills and skills in demand is essential to helping workers.
Algorithms, not unlike those that help you find a compatible date, have the potential to match job seekers to jobs based on skills, interests, aspirations, and cultural fit. At the same time, algorithms can help workers identify skills gaps3 in their resumes—based on the skills most in demand or trending in job descriptions, and highlight training opportunities.
No clearer has skills matching through technology played out than with freelancers and the self-employed. While oft-cited as facilitating companies’ reduction of full-time hires, the inevitable reality is that companies are increasingly prioritising hiring flexibility. Digital job portals like Upwork and Guru provide crucial project- based job opportunities and income for workers whose specific skills may not justify full-time hiring.
Finding good matches between skills and skills in demand is essential to helping workers.
Digital labour platforms like LinkedIn or CareerBuilder also create more transparent job markets and disrupt previously closed labour markets by increasing workers’ access to a wider variety of jobs and employers’ access to a wider pool of job seekers, reducing the advantage of “old boys clubs”, often driven by wealth and connections. Google’s new built-in job search engine goes one step further to aggregate available jobs across major online job boards, using AI to rationalise among duplicate listings and letting workers shortlist jobs by location.
The playing field is levelled even further by technology platforms that attenuate hiring biases such as paper qualifications and gender, by enabling testing for the specific aptitudes required on the job. Platforms like Codility and GitHub help employers seek out and test the quality of actual coding and development skills, not certifications. Catalyst DevWorks’ Catalyst Talent Platform4 uses machine learning on thousands of variables from hundreds of thousands of individual engineer and developer candidates to identify innate capabilities and predict whether someone will be exceptional talent in the job, whether or not they have a degree or a good resume.
REAL-TIME, REAL-WORLD TRAINING
Lifelong learning is much easier said than done.5 Massive open online courses (MOOCs) have already democratised learning, providing easy access to countless new courses and possibilities. However, much of this learning remains theoretical and does not train or test “on-the-job”, so it is less useful for industries such as manufacturing.
Nor will upskilling necessarily get you a job. I’ve had the unenviable position of speaking to an electronics engineer who was retrenched. In tears, he related how he tried to take professional courses in the biomedical sector, with hopes of entering what was then one of Singapore’s growth sectors. Despite his burnished qualifications, all the companies he approached felt that he did not have the job experience commensurate with someone else his age in the industry.
Virtual and augmented reality (VR and AR) open up new possibilities for “on-site”, “hands-on” training, and might provide a solution to learning that accelerates job transition, enhancing meaningful skills acquisition throughout one’s life. In manufacturing for instance, AR smart glasses that overlay computer-generated graphics and real-time instructions can improve productivity without prior training.6 This will shorten the time required to induct new workers and close skills gaps.
Significantly, these upskilling technologies can also help companies “test” out potential employees during the hiring process in a simulated environment, assuring them that the job seeker—even if they did not have prior work experience—can perform to standard. Real-time, real-world training with AR will also help existing workers learn continuously and at an accelerated speed, increasing organisational learning agility.
Using technology to help mitigate the impact of job displacements can only be truly effective if adopted at scale.
Whither Government’s Role?
Using technology to help mitigate the impact of job displacements can only be truly effective if adopted at scale. This can be challenging. For instance, identifying in-demand skills across sectors or on a national level, or skills matching through data analytics, will be most robust if there is open access to large volumes of job offerings on the demand side. The more source data available, the more transparent the market.
However, much of this information is fragmented across various platforms and job portals—with a significant proportion of hiring still done through personal referrals or head-hunters. Governments are best placed to address this by facilitating greater job data sharing, not only from the perspective of supply but also of demand. National job portals, where all employers are required to list job openings with job descriptions and skills needed—such as Singapore’s national online Jobs Bank—would go some way to open up demand-side data. Analyses of data aggregated across job centres such as the Employment and Employability Institute and grassroots’ Job Placement Centres can then offer a more comprehensive picture, not just of jobs sought but skills available on the supply side. This skills marketplace would also help Government, companies and workers identify skill gaps.
Technologies such as VR and AR for training are most impactful if they can both be customised and scaled up. In the near term, cost constraints and access to these technologies will limit their scalability. Governments could view these technologies not only as training tools but also as productivity enablers, when considering funding incentives.
Less quantifiable skills such as learning agility or strategic thinking may not be as easily evaluated through mediated platforms.
Technology will also affect various constituents to differentiated degrees. Eliminating biases through Codility or GitHub, for example, is limited to skills that are more quantifiable and thus demonstrable on a platform. Less quantifiable skills such as learning agility or strategic thinking may not be as easily evaluated through mediated platforms. These challenges are more tricky to address and certainly worth a separate discussion. Programmes such as Singapore’s Career Support Schemes and other work trials aim to cut through some of this opacity, but these are limited to older workers and the longer-term unemployed, and may lead companies to “discount” these workers’ value or deter companies from signing up to begin with, because they know support is time-limited.
More generally, Singapore could rethink and structure unemployment benefits in the context of more frequent gaps in employment and shorter employment cycles, while facilitating a more flexible labour market. [See “Trends and Shifts in Employment: Singapore’s Workforce” by Augustin Lee of this issue.]
Technology disruption often takes on the complexion of inevitability. While disruption will happen, companies and governments can be more thoughtful about why and how they implement technologies.
Technology as a Force for Social Resilience and Collective Progress
The discourse on technology disruption often takes on the complexion of inevitability. While disruption will happen, that workers will be worse off is not a foregone conclusion. Companies and governments can be more thoughtful about why they implement technologies, make better choices on which technologies to implement, and how these technologies are implemented. Throughout history, human progress has been defined by the choices that we have collectively made. By choosing technologies that best complement our workers instead of those that are simply the hottest or ostensibly most cost-effective, technology can be a force for collective progress. We would do well to think about how best to incentivise these choices sooner rather than later.
By deliberately harnessing technologies in these ways, we will be negotiating a new narrative: one that empowers workers and shows them that they too have a stake in our collective progress. Technology then no longer divides, but instead buttresses society’s resilience. It would offer Auntie Sally a vision of progress that she can once again take pride in contributing to and being part of.
An earlier version of this article first appeared on the blog Technology and Public Good.7
ABOUT THE AUTHOR
- See, for example: Kristin Lee, “Artificial Intelligence, Automation, and the Economy”, December 20, 2016, accessed May 5, 2017, https://obamawhitehouse.archives.gov/blog/2016/12/20/artificial-intelligence-automation-and-economy.
- David Rotman, “How Technology Is Destroying Jobs”, MIT Technological Review, June 12, 2013, accessed May 5, 2017, https://www.technologyreview.com/s/515926/how-technology-is-destroying-jobs/.
- Tang See Kit, “Faster, Better Job Matches? Singapore Start-ups Look to Redefine Career Searches”, Channel NewsAsia, April 25, 2017, accessed May 5, 2017, http://www.channelnewsasia.com/news/business/faster-better-job-matches-singapore-start-ups-look-to-redefine-8787100.
- Catalyst IT Services, “Catalyst Talent Platform”, accessed May 5, 2017, http://126.96.36.199/content/catalyst-talent-platform.
- “Equipping People to Stay Ahead of Technological Change”, The Economist, January 14, 2017, accessed May 5, 2017, http://www.economist.com/news/leaders/21714341-it-easy-say-people-need-keep-learning-throughout-their-careers-practicalities.
- Magid Abraham and Marco Annunziata, “Augmented Reality Is Already Improving Worker Performance”, Harvard Business Review, March 13, 2017, accessed May 5, 2017, https://hbr.org/2017/03/augmented-reality-is-already-improving-worker-performance.
- Karen Tay, “Technology and Public Good”, accessed May 5, 2017, https://techandpublicgood.com/.