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Of the many fields where AI and machine learning are beginning to make an impact, disaster preparedness and relief possess potential for them to do a lot of good. Implemented in the right ways to assist and inform first responders, as well as communities affected, AI can be leveraged to save lives in disaster situations.
AI In Forecasting and Predicting Natural Disaster
One of the primary strengths of AI is the way it increases our capacity to predict, and therefore plan for, events and circumstances. Considering that one of the most important ways to save lives in disasters is to have early warning, there’s a lot of good the technology can do.
As Craig Fugate, the former head of FEMA, put it in an interview with Scientific American:
“What really sold it to me is how much data is involved, and how we can see things at high-resolution, and quickly. We can run various scenarios in the days before a storm arrives and understand when and how systems would fail. Using AI lowers the threshold to do the ‘what-ifs.’”
The potential of AI isn’t just in predicting that a disaster will happen but in predicting where it will hit hardest, which defensive systems are likely to fail, and which communities are in the most danger. This information can be used to improve decision-making about the issuing of building permits and insurance.
Especially with natural disasters such as hurricanes, floods, tsunamis, and earthquakes, natural disasters often hit poor communities the hardest. Man-made disasters like oil spills do as well, a fact exemplified by the Flint water crisis. One of the challenges that Fulgate mentions is in training AI with datasets that do not contribute to erasing and damaging poor communities.
In all disasters, there are so many variables that are constantly shifting, AI may be able to come to conclusions that humans miss. For example, accounting for the effects of seasonal mosquito populations when classifying viruses like Zika has been an issue of contention. There are disagreements about its classification by the World Health Organization (WHO), which declared an end to the virus’ status as a global health emergency, but some analysts are worried the reclassification may have been too soon.
AI could prove absolutely invaluable to tracking and preparation efforts, as well as the updating and maintenance of critical equipment and defenses.
AI On the Ground During a Disaster
In recent years, when natural disasters struck, people on the ground have often turned to social media and ad-hoc volunteer groups in addition to, and sometimes instead of, relying on aid from the government or traditional charitable organizations. Communities have proven that they’re capable of banding together in the face of disasters and aid that doesn’t come quickly enough.
The Mexico City earthquake of 2017 was one example of the ubiquitous use of social media for the organization of citizen volunteers to save lives. People used social media to effectively crowdsource rescue operations, putting up requests for items and assistance, which were then distributed by volunteer groups, and then social media was used to coordinate meetings and deliveries.
Most social media platforms already rely on machine learning algorithms, but additional AI functionality could be of great assistance during disasters, helping both ordinary people and first responders keep up to date and organized. Deployed in the right way, existing timeline algorithms could be used to deliver and distribute information where it’s most needed. Or, AI could be used to scrape information from millions of social media posts and clue rescue workers in to the hardest hit areas and people in the most need.
AI After the Disaster: Recovery Efforts
After a disaster, insurance providers are inundated with claims. This often creates nightmarish delays for people who rely on insurance payments to get their lives back together. This usually has a disproportionate effect on poor communities — the longer the delays on insurance claims, the more hardship they endure. Delays in vehicle insurance claims could leave people unable to travel to and from work, or compel them to operate their vehicles in unsafe conditions. Delays in home insurance claims leave people displaced and in considerable financial hardship. AI could help sift through claims data, and help insurance companies identify high-priority claims, streamlining the process for everyone involved.
Robots Are Already on the Scene, We Need AI to Keep Up
Unmanned robots are being used to great effect in every aspect of disaster response. They can more accurately gather data about hurricanes without human pilots having to risk getting close; they can enter dangerous areas following a disaster to assess damage and look for survivors; and they can assist in rescue efforts.
To make the best use of robots and our ability to gather more data, our data collection and analysis techniques have to keep up. Receiving more data only helps if we have the capability to draw conclusions about it to increase response time. Autonomous machines and AI algorithms, when combined, act as a significant force multiplier in our ability to protect people and property in the face of disaster.