In the small village of Ogba-Ojibo in central Nigeria, sitting at the confluence of two of the nation's largest rivers – the Niger and Benue – 27-year-old Ako Prince Omali is counting the steps carved out of the dirt, which lead down the loam-coloured banks of the river Niger. This river bank, dotted with tufts of spiky grass, is where villagers come to fish or wash produce and laundry. Just last week, three of the steps were submerged during one night of rain, which raised the water level by about five metres. Normally, you can count seven steps down into the river. Now, only four remain above the surface of the water, the sticks bracing the muddy steps having washed away in the deluge.

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.

When Hurricane Ian churned over Florida in late September, it left a trail of destruction from high winds and flooding. But a week after the storm passed, some people in three of the worst-hit counties saw an unexpected beacon of hope. Nearly 3,500 residents of Collier, Charlotte, and Lee Counties received a push notification on their smartphones offering $700 cash assistance, no questions asked. A Google algorithm deployed in partnership with nonprofit GiveDirectly had estimated from satellite images that those people lived in badly damaged neighborhoods and needed some help. GiveDirectly is testing this new way of targeting emergency aid in collaboration with Google.org, the search and ad company's charitable arm.

When the novel coronavirus reached Togo in March, its leaders, like those of many countries, responded with stay-at-home orders to suppress contagion and an economic assistance program to replace lost income. But the way Togo targeted and delivered that aid was in some ways more tech-centric than many larger and richer countries. No one got a paper check in the mail. Instead, Togo's government quickly assembled a system to support its poorest people with mobile cash payments--a technology more established in Africa than in the rich nations supposedly at the forefront of mobile technology. The most recent payments, funded by nonprofit GiveDirectly, were targeted with help from machine learning algorithms, which seek signs of poverty in satellite photos, and cellphone data.

It sounds like a dream: Poor villagers are handed money regularly every month, for several years, with no conditions attached. An American organization is currently testing the model in Kenya. When the village elder came to her in September to invite her to a meeting under the acacia trees, Norah Odhiambo was skeptical. Storm clouds were gathering above nearby Lake Victoria, the 34-year-old relates, and she set aside the machete she uses to clear brush from her neighbor's field for a few shillings a day. A new aid organization called GiveDirectly, the village elder said, would like to introduce itself.

In this April 2017 photo provided by GiveDirectly, GiveDirectly basic income recipient Irine Ogolla poses in front of her home with her son near Lake Victoria in Kenya. Hawaii is considering doling out universal basic income, where everyone gets a chunk of money with no strings attached. The idea has attracted supporters across the U.S. and elsewhere as technology leaders, elected officials and economists debate what our lives will look like when robots and machines take over more jobs that human beings have held for decades.

The village is poor, even by the standards of rural Kenya. To get there, you follow a power line along a series of unmarked roads. Eventually, that power line connects to the school at the center of town, the sole building with electricity. Homesteads fan out into the hilly bramble, connected by rugged paths. There is just one working water tap, requiring many local women to gather water from a pit in jerrycans.

REMEMBER the basic income grant South African labour unions, churches and NGOs campaigned for back in the 1990s and early "noughties", but on which Trevor Manuel's Treasury frowned on as a fiscal nonstarter? Silicon Valley A-lister Sam Altman thinks the US will have to adopt something like it within the next generation or two -- and he's not alone. Altman is founder and president of Y Combinator, the seed-stage tech investor that helped launch Airbnb and Dropbox. As co-chairman of OpenAI, he is working with Elon Musk to see that artificial intelligence, as it approaches and perhaps surpasses the human variety, benefits mankind. He believes that while technology will generate vast new wealth, it will in the process destroy much traditional employment without replacing it.