5 Challenges AI and Analytics are Poised to Transform

Here are some socioeconomic areas that artificial intelligence and advanced analytics are well positioned to address this year.

Lee Ann Dietz, Global Government and Smart Cities Practice Director, SAS

March 3, 2022

5 Min Read
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The events and strain of the past two years have seen many socioeconomic challenges exacerbated. While the solutions are complex and daunting, AI and analytics can play a critical role in accelerating and delivering success. Leaders across government and industry have been empowered by trusted data to make rapid, cost-effective, and accurate decisions. For the year ahead, we’ve identified five social, economic, and political challenges that AI and analytics are poised to improve.

1. Infrastructure

The bipartisan Infrastructure Bill provides $550 billion to improve America's infrastructure over five years, impacting everything from bridges and roads to the nation's broadband, water, and energy systems. Government leaders must make smart use of data -- guided by AI and analytics -- to prioritize and optimize infrastructure investments.

For example, real-time data gleaned from IoT sensors on bridges, tunnels, roads, and other critical infrastructure, as well as surveillance video from drones, helps government organizations analyze the condition of these assets and anticipate when assets will fail. That allows agencies to make targeted and pre-emptive repairs. Additionally, cities can integrate public health data with data on the location of structures with lead pipes to assess risk and prioritize pipe replacements. Ultimately, these investments save money and keep citizens safer.

2. Fraud

About $3.5 trillion: That is roughly how much COVID relief money the US government has put in play since the pandemic began. Bad actors seize on chaos and confusion, so it is not surprising that certain types of fraud exploded during the pandemic. The US Secret Service estimated that criminals have stolen close to $100 billion in pandemic relief funds. That money is difficult to track and recover but also is diverted from those with legitimate claims who need it most.

AI and data analytics are critical to addressing this challenge, if used the right way. Currently, facial recognition and data matching (or identity quizzes) are used by government agencies to confirm identities. However, these technologies can present inequity and access issues. Facial recognition has often struggled to accurately identify individuals with darker skin tones. Identity quizzes rely on credit history-based questions such as type of car owned, previous permanent addresses, credit card type and banking history. These requirements may negatively impact low-income individuals, as well as the young, unbanked, immigrants, etc.

This year, AI and analytics will deliver for agencies combating fraud through a more holistic approach that draws on identity analytics from data sources that do not carry such inherent inequity and access bias, including digital devices, IP addresses, mobile phone numbers and email addresses. Data-driven identity analytics is key to not only identifying and reducing fraud but also reducing friction for citizens applying for legitimate unemployment insurance benefits.

3. Climate change

The infrastructure bill includes $50 billion to help communities fight the effects of climate change, including efforts to protect against drought, extreme heat, and floods. Analytics has a critical role in informing us about climate impacts, planning for what’s ahead and raising awareness about how to promote sustainability and help our environment.

The increasing volume of relevant data from sensors and IoT demands the use of advanced analytics and AI to address climate challenges. For example, the Town of Cary in North Carolina prepares for floods through a system of sensors installed in local creeks. Using AI and IoT technologies in the cloud, Cary can quickly predict where flooding will occur and how extensive it will be, so the town can deploy resources and alert downstream communities.

4. Physical and mental health

The pandemic’s impact extends far beyond those who contract COVID-19; the physical and mental toll is felt by millions more. In 2022, look for AI and analytics to support new treatments, better testing, and a better understanding of public health inequities to ensure that people can stay safer and healthier, regardless of socioeconomic factors.

We’re already seeing these efforts by users of the COVID Research Database, the largest pro bono COVID data resource and research environment in the world. Armed with free AI tools, researchers are exploring the ways COVID manifested among populations of color.

The University of North Carolina at Chapel Hill’s Rapidly Emerging Antiviral Drug Development Initiative (READDI) is using machine learning to analyze data from the deep lung environment of severely ill COVID patients to identify targets for new COVID antiviral drugs.

On the testing front, new low-cost technologies will change the game for future pandemics by providing instant test results using disposable test strips and a handheld reader. By applying AI and visual analytics, users can mine diagnostic data and electronic health records to uncover infection trends and visualize disease hot spots to better monitor and predict outbreaks

5. Law enforcement and public safety

Law enforcement leaders are seeking innovative policy reforms and technologies that address public demands for heightened accountability and transparency. We expect 2022 to see more police departments turn to evidence-based policing (EBP) -- anchored by analytics -- to improve officer well-being, reduce crime rates, and close the confidence gap in policing.

Instead of measuring outputs, such as open warrant sweeps or traffic enforcement details, people arrested or community relations activities, EBP measures the impact of those activities, such as whether the overall volume of crime or traffic accidents decreased. It also analyzes the effects of interactions with the public, whether positive or negative.

By looking at the whole officer, highlighting their good work, looking at external and internal data impacting the officer and their community, police departments will be able to improve transparency, accountability, and connections with the community.

About the Author(s)

Lee Ann Dietz

Global Government and Smart Cities Practice Director, SAS

Lee Ann Dietz evangelizes the use of data and analytics for transportation, smart cities and other public sector priorities. With more than 25 years supporting customers with analytical solutions, she understands the possibilities and promise of data to help citizens, bolster efficiencies and improve quality of life. She holds an MBA from the Darden Graduate School of Business at the University of Virginia and a bachelor's degree in Economics from Stanford University.

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