“Using Algorithms to Deliver Disaster Aid”
Communications of the ACM, June 2023, Vol. 66 No. 6, Pages 17-19
By Keith Kirkpatrick
“Ensuring the billions of dollars governments sent the disaster-stricken actually get to their intended destinations.”
Over the past decade, machine learning-based algorithms have been deployed across a wide range of use cases and industries. From the algorithms that assess an individual’s creditworthiness, to algorithms that serve up suggested movies and shows to watch on Netflix, the impact of Big Data, analytics, and automation are felt daily by nearly everyone.
One area of life where algorithms have not yet been perfected is with payments made by government or relief organizations to people in the aftermath of a crisis, emergency, or natural disaster, where getting financial relief to the people who need it most is critical. Though there have been pilot programs and limited use of artificial intelligence (AI) to provide targeted aid, the practice is far from widespread.
Key drivers behind the desire to incorporate more automation and data analysis into aid dispersion is the time-consuming nature of assessing who is eligible to receive aid, and then ensuring that aid is only delivered to those legitimate recipients. Due to the scale and compressed timeframes of delivering aid in the wake of a disaster or emergency, many organizations have struggled with this process.
For example, U.S. federal agencies made approximately $281 billion in payment errors during fiscal year 2021, up $75 billion from the previous fiscal year, and nearly double the amount reported in 2017, according to data from the U.S. Government Accountability Office. Errors in the distribution of funds under unemployment insurance and small business loan programs contributed to that total, driven by the COVID-19 pandemic response.
However, it is not only government agencies that struggle. Non-government charitable organizations, many of which are actively involved in relief efforts when unexpected natural disasters occur, also face challenges, usually related to fraud, where scams are used to access funds that are earmarked for actual victims. A 2021 report from Western Union found the cost of financial fraud impacting non-government organizations reached an estimated $5.1 trillion in 2019, with just 9% of non-governmental organizations (NGOs) indicating they have some type of fraud awareness program in place, and 54% of NGOs saying they do not report fraud to law enforcement because of the negative reputational impact it can have on future potential funding.
One of the ways in which technology can be used to mitigate fraud is by correlating the level of impact of a disaster or emergency condition with the people who have been affected. In September 2022, Hurricane Ian struck the state of Florida, resulting in significant damage from high winds and flooding across several counties. Instead of asking residents to fill out lengthy forms to receive aid, which requires a significant amount of time and effort to reach out to all possible recipients to determine eligibility, a pilot program conducted by the nonprofit GiveDirectly utilized an algorithm to help direct aid to nearly 3,500 residents of Collier, Charlotte, and Lee counties.
To make the initial assessments of which areas needed assistance, a mapping tool called Delphi was developed by four Google machine learning experts who worked with GiveDirectly over a six-month period. Delphi is used to overlay live maps of storm damage onto data on poverty from sources including the U.S. Centers for Disease Control and Prevention (CDC) to pinpoint communities that are likely to be in need after disasters. The storm damage data is provided by another Google tool called Skai that uses machine learning (ML) to analyze satellite imagery from before and after a disaster, then estimates the level and scope of damage to buildings.
To train the ML algorithms powering Skai’s damage assessments, satellite images of hundreds of buildings in the disaster area are manually labeled, so the algorithm can then identify similarly damaged buildings across the entire affected area using unlabeled images. According to the company, the tool was 80% more accurate than manual assessments when it was used on the Beirut, Lebanon, port explosion in 2020, and in the aftermath of Cyclone Yasa in Fiji in 2021.
To provide aid to the people who needed it after Hurricane Ian, GiveDirectly sent a push notification to users of a benefits app called Providers, which manages food stamp payments. By taking the correlated damage assessments from Skai and identifying the people who used Providers who lived in affected areas, the program was able to offer $700 in aid directly to people who needed it. As of October 2022, 900 people had accepted the offer; if all 3,500 eligible recipients were to accept the offer, $2.4 million would be paid out in direct financial aid.
One of the challenges with using algorithms and automation to deliver cash aid to victims is that, in an era where spam and phishing scams are ubiquitous, people who genuinely need assistance may think an unsolicited offer of aid is too good to be true and simply a scam. Indeed, GiveDirectly conducted a test in September 2022 after Hurricane Fiona in Puerto Rico, sending out push notifications informing recipients about the availability of an immediate cash benefit to 700 people, but less than 200 people took up the offer. Sarah Moran, GiveDirectly’s U.S. director, told Wired, “That was a lower response than we would have expected,” blaming the low uptake on people suspecting the messages were a phishing campaign.
Still, the use of AI and machine learning to deliver aid is not commonplace; GiveDirectly’s Hurricane Ian efforts were the first observed use of this type of technology in the U.S. However, other organizations are realizing the value of using technology to provide disaster or other emergency assessments and disbursing financial and other forms of relief.
About the Author:
Keith Kirkpatrick is principal of 4K Research & Consulting, LLC, based in New York, NY, USA.
- Coronavirus Oversight, U.S. Government Accounting Office.
- What NGOs need to know to prevent fraudulent activities?, Western Union, May 21, 2021.
- Machine learning and phone data can improve targeting of humanitarian aid, Nature, March 31, 2022.