Government algorithms, though largely obscure to the public, are having a radical and visible effect on people’s lives around the world. In some cases, they are making life-changing decisions, impacting millions of people. Until now, these algorithms and predictive systems have been shrouded in mystery, hiding how and why they produce the results they do.

For the first time, we’re taking a close look at one such government-employed algorithm and revealing how it works. This algorithm, which is employed by governments across the world, uses a mix of machine learning, artificial intelligence, and predictive models to gather and analyze intelligence from numerous sources. By analyzing data from millions of public records, digital interactions, and open-source documentation, the algorithm can make decisions about the eligibility of citizens for certain government programs, predicting outcomes such as whether an applicant will be granted a loan or pass an upcoming exam.

The algorithm is designed to identify patterns and trends in data, helping the government to reach decisions quickly and accurately. Through machine learning, the algorithm can become increasingly effective at predicting outcomes and ultimately making more informed decisions. By monitoring large-scale data sets, it can detect subtle changes in behaviour and spot emerging trends.

In addition to its data-driven decision-making capabilities, the government algorithm also assesses the behaviour of applicants to determine their eligibility. For example, if the algorithm picks up on consistent delinquent payments, it could flag a potential borrower as a poor credit risk.

We hope this glimpse into the inner workings of a government algorithm can provide some insight into how these services can improve decision-making and bolster governments in their efforts to make the best decisions possible. As technology continues to evolve and move forward, we believe complex decision making algorithms like these will become increasingly commonplace in the public sector.