Machine learning is mentioned in the same breath as automation and AI. But what is machine learning, really? And can algorithm-powered systems really transform the public sector, driving positive outcomes for citizens?
Machine learning is well accepted in the private sector as a means of processing massive volumes of customer data. However, there is sometimes an uneasiness around machines making decisions about our health and well-being. Delivering a bad medical prognosis or denying benefits doesn’t sit well without a human touch.
Augmenting, not replacing, human decisions
Yet I believe there’s a strong case for greater adoption across many different use cases. The basic premise of machine learning is that we teach a computer to spot patterns in data. We feed it an algorithm for a specific task. Then it builds a model to uncover connections in the data, and we use that insight to inform our decisions.
For example, machine learning can analyse patients’ interactions in the health care system and highlight where the longest wait times are. It can also discover how to best deploy resources to save the most time for the most patients. Machine learning can identify areas for improvement in real time, far faster than humans. And it can do so without bias, ulterior motives or fatigue-driven error. So, rather than being a threat, it should be viewed as a reinforcement for human effort in creating fairer and more consistent service delivery.
As Thomas H. Davenport, analytics thought leader, noted in The Wall Street Journal, "Humans can typically create one or two good models a week; machine learning can create thousands of models a week."
The iteration factor
Machine learning is an iterative process. As the machine is exposed to new data and information, it adapts to it independently through a continuous feedback loop, which in turn provides continuous improvement. As a result, you can produce more reliable results over time and improve decision making.
It’s comparable to "black-box thinking" in aviation. By openly recognising problems and then feeding that knowledge back into its processes, the industry is able to keep improving safety and avoid repeating previous failures.
Machine learning can deliver the same type of capabilities within public services. By analysing real-time data and interactions to spot areas for improvement, you can help prioritize support for the most vulnerable citizens and drive better outcomes.
A great example of this is the Cancer Center in Amsterdam, which is using machine learning to identify patients who would benefit most from certain treatments. They’re combining the results of CT scans with advanced genomics and analytics to assess how patients would respond to chemotherapy after surgery. This means patients avoid the stress of unnecessary procedures. And those who would benefit from treatments have shorter waiting times.
Decisions like these have a direct impact on people’s health and well-being. The faster decisions can be made the better.
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Fast data for faster outcomes
In the US, government researchers are using machine learning to help officials make quick and informed policy decisions on housing. They are using analytics to assess the impact of housing programmes on millions of lower-income citizens. And they can also drill down into factors such as quality of life, education, health and employment to generate accessible reports for government officials.
Machine learning capabilities have shortened the cycle times for these reports, getting information to key decision makers faster. Now they can enact policy decisions as soon as possible for the benefit of residents.
We should view the increased use of machine learning within the public sector as complementary to human decision making. It’s an assistive method that turns growing data volumes into positive outcomes for people, quickly and fairly. As the cost of computational power continues to fall, ever-increasing opportunities will emerge for machine learning to enhance public services and help transform lives.
To find out more about the use of AI and machine learning in government, please visit our dedicated hub here: sas.com/uk/gov.
If you’d like to connect for a discussion on how machine learning could help in your environment, please drop a note to Roderick Crawford, Public Sector Director UK, at Roderick.email@example.com.