African software developers using artificial intelligence to fight inequality

Determined to use her skills to fight inequality, SA computer scientist Raesetje Sefala set to work to build algorithms flagging poverty hotspots — developing data sets she hopes will help target aid, new housing or clinics.

From crop analysis to medical diagnostics, artificial intelligence (AI) is already used in essential tasks worldwide, but Sefala and a growing number of fellow African developers are pioneering it to tackle their continent’s particular challenges.

Local knowledge is vital for designing AI-driven solutions that work, Sefala said.

“If you don’t have people with diverse experiences doing the research, it’s easy to interpret the data in ways that will marginalise others,” the 26-year old said from her home in Johannesburg.

Africa is the world’s youngest and fastest-growing continent, and tech experts say young, home-grown AI developers have a vital role to play in designing applications to address local problems.

“For Africa to get out of poverty, it will take innovation and this can be revolutionary, because it’s Africans doing things for Africa on their own,” said Cina Lawson, Togo’s minister of digital economy and transformation.

“We need to use cutting-edge solutions to our problems, because you don’t solve problems in 2022 using methods of 20 years ago,” Lawson told the Thomson Reuters Foundation in a video interview from the West African country.

Digital rights groups warn about AI’s use in surveillance, but Sefala said it can also be used to “serve the people behind the data points”.

She mapped out every suburb and township in SA and then combined this data set with satellite data and machine learning algorithms to capture the growth of these neighbourhoods over time.

She soon realised the algorithms she had built only went so far because some townships — including where she grew up — were not being correctly predicted.

Being able to refine the algorithms based on her lived experience meant the data collected became more accurate.

“Those kinds of decisions determine who you alienate or include when you build your AI models,” said Sefala, the first AI research fellow at the Distributed AI Research (DAIR) institute — a community-driven research group.

‘DELIVERING HELP’

As Covid-19 spread around the world in early 2020, government officials in Togo realised urgent action was needed to support informal workers who account for about 80% of the country’s workforce, Lawson said.

“If you decide that everybody stays home, it means that this particular person isn’t going to eat that day, it’s as simple as that,” she said.

In 10 days, the government built a mobile payment platform — called Novissi — to distribute cash to the vulnerable.

The government paired up with Innovations for Poverty Action (IPA) think-tank and the University of California, Berkeley, to build a poverty map of Togo using satellite imagery.

Using algorithms with the support of GiveDirectly, a non-profit organisation that uses AI to distribute cash transfers, the recipients earning less than $1.25 per day and living in the poorest districts were identified for a direct cash transfer.

“We texted them saying if you need financial help, please register,” Lawson said, adding that beneficiaries’ consent and data privacy had been prioritised.

The entire programme reached 920,000 beneficiaries in need.

“Machine learning has the advantage of reaching so many people in a very short time and delivering help when people need it most,” said Caroline Teti, a Kenya-based GiveDirectly director.

‘ZERO REPRESENTATION’

Aiming to boost discussion about AI in Africa, computer scientists Benjamin Rosman and Ulrich Paquet cofounded the Deep Learning Indaba — a weeklong gathering that started in SA — together with other colleagues in 2017.

“You used to get to the top AI conferences and there was zero representation from Africa, both in terms of papers and people, so we’re all about finding cost effective ways to build a community,” Paquet said in a video call.

In 2019, 27 smaller Indabas — called IndabaX — were rolled out across the continent, with some events hosting as many as 300 participants.

One of these offshoots was IndabaX Uganda, where founder Bruno Ssekiwere said participants shared information on using AI for social issues such as improving agriculture and treating malaria.

Another outcome from the SA Indaba was Masakhane — an organisation that uses open-source, machine learning to translate African languages not typically found in online programmes such as Google Translate.

On their site, the founders speak about the SA philosophy of “ubuntu” — a term generally meaning “humanity” — as part of their organisation’s values.

“This philosophy calls for collaboration and participation and community,” reads their site, a philosophy that Ssekiwere, Paquet and Rosman said has now become the driving value for AI research in Africa.

INCLUSION

Now that Sefala has built a data set of SA’s suburbs and townships, she plans to collaborate with domain experts and communities to refine it, deepen inequality research and improve the algorithms.

“Making data sets easily available opens the door for new mechanisms and techniques for policy-making around desegregation, housing, and access to economic opportunity,” she said.

African AI leaders say building more complete data sets will also help tackle biases baked into algorithms.

“Imagine rolling out Novissi in Benin, Burkina Faso, Ghana, Ivory Coast ... then the algorithm will be trained with understanding poverty in West Africa,” Lawson said.

“If there are ever ways to fight bias in tech, it’s by increasing diverse data sets ... we need to contribute more,” she said.

But contributing more will require increased funding for African projects and wider access to computer science education and technology in general, Sefala said.

Despite such obstacles, Lawson said “technology will be Africa’s saviour”.

“Let’s use what is cutting edge and apply it straight away or as a continent we will never get out of poverty,” she said. “It’s really as simple as that.” 

Thomson Reuters Foundation