Machine learning is an umbrella term for a computer identifying patterns and relationships that exist within data. Image: Similar images/Shutterstock.com
Concerns about the impact of technology on the workforce are nothing new, going back to the advent of weaving machines in the 17th century, continuing through the Industrial Revolution all the way to the rise of technologies like AI and machine learning.
When times are good, and economies are thriving, technology and machines are valued for increasing productivity, allowing businesses to invest in upskilling their employees. But when recession hits and companies pull back spending due to inflation, machines are often blamed for displacing workers. In fact, the recession of 1958 was termed by one newspaper as the “Automation Depression” because new machinery was blamed for replacing workers.
However, this current economic slowdown is different. We have a worldwide labor shortage. In fact, recent studies show that there will be an estimated shortage of 85 million workers across the globe by 2030, potentially resulting in $8.5 trillion in unrealized annual revenues.
So, rather than blaming automation as we face this economic slowdown, many are crediting advancements in technology for driving business and helping to fill the void created by this labor shortage.
According to a CNBC Technology Executive Council survey, business leaders have learned from past downturns that technology is a business driver rather than a cost center. The areas in which they are focusing investments include cloud computing, machine learning and artificial intelligence, and automation.
While businesses have heartily embraced these new technologies, governments have been slower to do so. The governments that have adopted these technologies are able to tackle seemingly impossible tasks, run more effectively, and provide faster, better services to their citizens. Additionally, machine learning and computer automation are helping governments perform tasks with fewer man-hours, which enables them to continue to fulfill their mission despite the growing labor shortage that is hitting the public sector particularly hard.
Machine learning is an umbrella term for technology where the machine learns from the data it is provided by identifying patterns and relationships that exist within the data itself. With greater computational power than ever before, machines can now continuously analyze substantial amounts of data and transform what they have read, heard, and observed into intelligent insights at a tremendous operational scale.
Machine Learning Filling Government Labor Gaps Around the World
This is precisely what the city of Istanbul, Turkey is benefitting from in its Smart City project. With a population of 15 million people, Istanbul has the worst traffic congestion in the world, according to the Tomtom Traffic Index. Istanbul Metropolitan Municipality is applying artificial intelligence, machine learning, and data analytics to predict congestion and optimize Istanbul’s transportation data efficiently.
As a result, they have been able to reduce travel times, reduce fuel consumption, and reduce pollution, which improves citizens’ quality of life. Because much of the data is processed by machines, tremendous insights are being revealed without requiring a huge staff of manual workers.
Tax assessment offices, such as those in Wake and Mecklenburg Counties in North Carolina, are also turning to advanced technologies to assist them in their work. With the aid of machine learning which identifies patterns and trends within huge amounts of data, every residential property in their communities is revalued every night. With hundreds of thousands of properties, each with dozens of characteristics, this would be impossible for humans to accomplish. But, the machine learning model performs the valuations in under an hour, saving hundreds of hours of work by assessors and making their job easier. The model also produces market trends, identifies comparable properties, and highlights outliers – all of which gives the assessors information they need to ensure that properties are being valued appropriately and fairly.
The ability to quickly process massive amounts of data to identify patterns and trends also makes machine learning ideal for fighting fraud and increasing compliance. Using AI, Belgium’s Ministry of Finance reduced the loss of funds related to one type of fraud by 98%, from 1.1B € to .029 B €. In one Asian country, the Bureau of Labor Insurance replaced its traditional, manual method for detecting fraudulent claims with machine learning. From their huge amounts of records, the models identify criminal trends, create rules based on the data, and then isolate instances of loss due to fraud or error. These machine learning models more accurately prioritize cases so that investigators spend less time doing tedious work.
Automation Success Requires the Human Element as its Foundation
Machine learning performs tasks that were previously inconceivable – such as detecting patterns between millions of data points. These benefits, and others, require less labor which, as we face our global labor shortage, will become increasingly important.
Will the machine replace the worker completely? No, but it will free workers from having to perform mundane or overly complex tasks. At the end of the day, control remains with the human being, even if there are multiple platforms and services doing things in a completely automated manner. Human beings remain at the core of success for AI/ML automation. Workers will be able to invest their time in training and upskilling, and remain key in providing the critical functions and services that machines cannot do: apply common sense, intuition, creativity, and empathy to run programs, deliver services, make decisions, and set policies.
Jennifer Robinson works to help local governments maximize the use of their data through data integration, data management, and analytics. Jennifer has a background in software development and local government. She co-wrote the book A Practical Guide to Analytics for Government and is featured in the book Smart Cities, Smart Future. She has served as a Councilwoman for Cary, NC since 1999 and is a member of several boards that promote the success of her region and state as well as cities, counties, and councils of governments. Jennifer advocates for the use of emerging technologies to make local governments more effective.
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