Artificial intelligence and machine learning algorithms are helping the U.S. government deliver better results to taxpayers. Two use cases — worker safety and equipment maintenance — showcase these benefits.
Before we explore those areas, let’s review the basics of AI/ML. AI engines use complex algorithms that scan huge databases to find patterns that reveal insights. Over time, the algorithms learn by prioritizing positive outcomes and discouraging negative results. Here’s a quick look at two examples:
Making Workers Safer and More Efficient
Federal regulators have two critical worker-safety concerns: reducing workplace risks and keeping federal workers safe. And because they have limited budgets, regulators need to help their workers get more work done in less time.
DMI’s work with the Mine Safety and Health Administration illustrates how AI/ML can improve safety and efficiency. We contracted with MSHA to make mine inspections more efficient. Before this project, MSHA inspectors carried laptops, digital cameras and thick books of regulations into the depths of the nation’s mine shafts. Even with these modern tools, inspections were cumbersome and time-consuming. Some inspections took months to complete by the time all the paperwork was wrapped up.
Our project digitized the inspection process and sent inspectors to work with Microsoft Surface tablets that stored digital versions of the regulations. With new tools and methodologies, inspection timelines shrank significantly, reducing the time it takes to convert findings into actions from months or weeks to days.
AI technologies play a key role in this process. We can use AI to comb inspection data, correlate with other data source, and develop predictive models suggesting when accidents or outages might happen in a mine. We can use these findings to coach mine owners on proactive maintenance and measures to reduce the risk of accidents.
With AI and digital technologies, we can make mines safer for miners and the people who inspect them. And the increased efficiency means inspectors can visit more mines and identify more safety risks.
Predicting Equipment Maintenance
The federal government’s equipment inventory is mind-boggling. The Pentagon springs immediately to mind with the U.S. military’s armada of high-tech weaponry and its fleet of cars, trucks, planes and other vehicles. But the feds also own data centers, distribution lines, medical equipment and hundreds more varieties of machinery.
All of these devices face everyday wear and tear. Some endure extraordinary stress in hostile environments. Predictive AI algorithms can tell field commanders that their tanks need to come in for repairs to prevent breakdowns on critical missions.
The same concept applies across the government’s inventory: Replace worn parts in conditions you control. Then you don’t have to deal with, say, a blown engine or transmission that can cause damage far beyond the site of the breakdown.
Bringing Speed, Safety and Insight to Government
Government agencies answer to a demanding public. Reams of regulations encourage safe practices and discourage risky behaviors. Data-governance rules require extra effort to keep personal information secure.
These forces naturally slow the progress of federal technology projects. Indeed, some timelines become so elongated that IT solutions are obsolete when the government starts using them. It doesn’t have to be that way. As DMI learned with projects such as MSHA, mobile technologies, the cloud and AI algorithms make it possible to speed up timelines while improving service to the public.
The government will probably never be as nimble as the public sector. After all, agencies have to reconcile competing demands on a scope rarely seen in the private sector. But our experience shows immense potential to use AI/ML to do the public’s work while elevating safety and efficiency.
It’s become popular to fret over the future of AI. While advanced automation may prove sinister in the decades to come, for now AI is taking repetitive chores off of people’s to-do lists and helping them get more work done in narrow time frames. That’s a change for the better in the government realm.
— Varun Dogra, chief technology officer