The large volumes of data handled by government agencies makes them perfect candidates for management by artificial intelligence. AI systems must be carefully designed by experts relevant to that field & AI in public services should be governed by a strict ethical framework.

Copyright by

SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningIn a time when unprecedented volumes of public funds are being distributed for COVID relief programmes, the efficiency of public services has never been more important. From fraud and error detection to the prediction of service disruptions, the variety of data held by government agencies means that they are well positioned to use artificial intelligence – which relies on good-quality data as a vital component for success – to address these problems.

Governments are just starting to explore the potential of AI to transform public services. It is crucial to design systems to capture the right data at the outset, so that AI can be deployed efficiently. This will all be made possible by tailoring systems to the subject matter at hand, with the help of policy-makers, public servants and data scientists, all working together to fully realize the benefits of this technology.

Governments are in a unique position of power, with access to a wide range of sensitive data. The use of AI in public services will need to operate within a robust ethical framework, supported by strong security and clear understanding of its place in decision hierarchies. Here are seven ways governments today are using AI to improve public services:

1. Reducing fraud and error in the tax and benefits systems

Governments today can benefit from the application of anomaly detection to benefits claims and tax rebates. The Department of Work and Pensions in the UK currently estimates £4.6 billion in overpayments and £2 billion in underpayments in the benefits system, while HMRC’s latest estimates of the tax gap stand at £31 billion. The application of machine learning to the vast data-holdings of these organizations to reduce these figures is underway, with much progress to be made in the coming years.

2. Detect grant fraud

Grant monies from the government are often issued with specific criteria for how the money is supposed to be spent. Detecting whether funds are being used as intended can be a challenge, due to the number of grants requiring review. Testing whether grant conditions are met often requires analysis of written reports, which are not always provided in a machine-readable format. To solve this problem, multiple machine learning methods can be applied to extract text, process it and classify it in order to determine a risk profile.

Read more: