Tech giants are using artificial intelligence (AI) and machine learning (ML) to create innovative strategies to meet the United Nation’s Sustainable Development Goals and eradicate global poverty by 2030. A central barrier to development in third-world nations is in-access to high-quality, timely and accessible data.
Big data platforms like AI expand capabilities to acquire accurate, real-time, micro-level information, while ML allows pattern recognition at a macro-level. Combined, these data advances can make data more accessible, applicable and finely scalable while accelerating the speed and scale for private and public development actors to implement change. Companies are partnering across public, private and nonprofit sectors to broaden the collective impact.
Take a look at the innovative approaches tech giants are taking to help global poor communities with data and what the incorporation of AI technologies means for the future of global poverty initiatives. These approaches aim to employ AI to meet the SDGs within its allotted time frame.
Education and Digital Training
On June 19, 2019, the day preceding World Refugee Day, Microsoft announced the inception of two projects partnering with Asylum Seeker Advocacy Project (ASAP) and Kids in Need of Defense (KIND). These projects supplement its AI for Humanitarian Action group to help incorporate AI to meet the SDGs.
The AI for Humanitarian Action group is a $40 million, five-year program part of Microsoft’s larger AI for Good suite (a $115 million, five-year project). The projects will provide AI tools to help staff track court dates, prioritize emergency cases and translate for families with AI speech-to-text. Microsoft also has continuing partnerships to incorporate AI/ML into educational services for refugees with the following groups:
- International Rescue Committee (IRC): This committee works to provide humanitarian aid through the creation of sustainable programming for refugees, displaced populations and crisis-affected communities. This includes career development programming and digital skills training to empower refugees and make them relevant for the job markets in each affected country. Microsoft and IRC’s Technology for Livelihoods in Crisis project in Jordan is an example of this.
- United Nations Children’s Fund (UNICEF): Together with the University of Cambridge, the UNICEF is developing The Learning Passport. The digital platform will ensure better access to education and facilitate learning opportunities for youth displaced by conflict and natural disasters. It creates scalable learning solutions tailored to each child. Crises have affected the quality of education for 75 million youth.
- Norwegian Refugee Council: This council is providing an AI chatbot service that uses language understanding, machine translation and language recognition to deliver high-quality education and digital skills training to refugees. This helps to close the education gap for the millions of youth affected by conflict. It will also help humanitarian workers communicate with migrants who speak other languages, which will help them best provide the best service.
- United Nations High Commissioner for Refugees (UNHCR): UNHCR plans to provide 25,000 refugees in Kakuma with access to high-quality, accredited, context-appropriate digital learning and training by 2020 for development in Kakuma markets. UNHCR intends this project to expand across multiple countries.
Food Security and Agricultural Development
The fact that farms do not always have power or internet security limits technological developments that address food security and agricultural development. Here are some efforts that consider the capabilities of farmers and the respective developing regions:
- Microsoft FarmBeats: It aims to enable data-driven farming compatible with both the capabilities of the farmer and the region. FarmBeats is employing AI and IoT (Information of Things) solutions using low-cost sensors, drones and vision and ML algorithms. This combined AI and IoT approach enables data-driven improvements in agriculture yield, lowered costs and reduced environmental impacts of agricultural production, and is a significant contribution to help AI to meet the SDGs.
- Apollo: Apollo uses agronomic machine learning, remote sensing and mobile phones to help farmers maximize profits in developing markets. Apollo delivers scalable financing, farm products and customized advice to farmers while assessing the farmers’ credit risk. Apollo customizes each product in order to double farm yields and improve credit. The beta project is starting in Kenya.
- The International Center for Tropical Agriculture (CIAT)/CGIAR research group: It aims to implement preemptive solutions rather than reactive solutions to end hunger and malnutrition by 2030. CIAT has developed a Nutrition Early Warning System (NEWS), which uses machine learning to make predictions on malnutrition patterns based on current and future estimates of crop failures, droughts and rising food prices. This approach is able to detect an impending nutrition crisis and take action instead of responding after the crisis has taken hold.
Socioeconomic Data Collection
According to a report by The Brookings Institute as a part of its “A Blueprint for the Future of AI series,” data providing national averages “conceal more than they reveal” and inaccurately estimate and map patterns of poverty. Survey data is often entirely unavailable or otherwise low in quality in many of the poorest countries where development needs are greatest. 39 of the 59 countries in Africa conducted less than two surveys between 2000 and 2010.
Even in large countries with sophisticated statistical systems, such as India, survey results remain inaccurate, with the gap between personal reporting and national accounts amounting to as much as a 60 percent difference in some countries. Companies are addressing this by utilizing big data from remote sensing satellites.
The Group on Earth Observations (GEO) is using Earth Observations (EO) to provide finely-tuned and near-real-time data on economic activity and population distribution by measuring nighttime luminosity. Researchers have noted a correlation between luminosity and GDP as well as subnational economic output. Collecting socioeconomic data in this way can ensure higher quality data important to policy implementation and direction to countries with the greatest development needs.
Timothy Burke and Stan Larimer launched Sovereign Sky in 2018, putting satellite data into action. Sovereign Sky is the world’s first space-based blockchain which provides secure private internet networks and powers a new Free World Currency to redistribute the world’s wealth with a goal of eradicating poverty by 2032.
The eight current satellites cover Africa and India and the organization will send boxes of StealthCrypto phones, digital wallets, smart cards and modems to people in need. Sovereign Sky will deploy 36 satellites within three to 10 years to cover the entire world in a secure blockchain internet connection, closing the gap on technological interactions between all nations and including the world’s remotest and poorest areas in internet connectivity.
Pitfalls of AI-Driven Global Development Initiatives, and Moving Forward
AI and ML have crucial capabilities in reshaping education, agriculture and data collection in the developing world. However, these technologies have a history of producing unethical racial profiling, surveillance and perpetuating stereotypes, especially in areas with a history of ethnic conflict or inequality. AI and ML applications have to adapt in ways to ensure effective, inclusive and fair distribution of big data resources in the developing world. Development experts need to be in close collaboration with technologists to prevent unethical allocations.
This diversification is why it is important that tech giants like Microsoft, and projects like those by the ICAT/CGIAR, are created in collaboration with various nonprofit, public and private sector groups to ensure interdisciplinary ethical liability for big data applications in sustainable development contexts. Ensuring the use of AI technologies is context-specific to the affected regions and populations will help prevent misappropriation of the technology and increase quality and effectiveness.
Working with local companies and sectors can create long-lasting engagement and grow permanent technology sectors in the developing areas thus contributing to the local economy. These strategies can put forth effective, ethical and productive applications of AI to meet the SDGs.
– Julia Kemner