Meet the awardees of the data.org Inclusive Growth and Recovery Challenge!

The Inclusive Growth and Recovery Challenge Awardees Event

With the Inclusive Growth and Recovery Challenge, we looked all over the world for projects harnessing the power of data science to help people and communities thrive. From more than 1,260 applications, these eight proposals emerged as outstanding examples of data science for social impact. We are proud to announce them as our awardees!

We also wish to introduce here the recipient of a special award, in collaboration with the Paul Ramsay Foundation, for one outstanding project from Australia.

Cities and Towns

Mapping the regional quality of life

SUBMITTED BY

LOCATION

Denmark

There is an increasing regional divide, witnessed in Denmark just as in much of Europe and North America. As economic inequality between regions increases, this disparity leads to enhanced migration out of underserved communities. Those leaving these communities are more often highly educated, young adults moving to large urban areas to pursue education and jobs. These persistent patterns of who leaves and who stays behind reinforce regional differences. As a result, underserved communities often struggle to ensure local public services and economic security.

BUILD will provide public authorities and decision-makers with tools to compare areas, and identify those with less local economic opportunity. To spread the use of data-driven insights, they are building an interactive website containing key indicators of economic prosperity in any given area in Denmark. Indicators will include housing prices, human capital, local wage levels, transportation, migration rates, and local amenities. The website will feature interactive maps and infographics illustrating the key insights, and give an overview of the regional differences.

Access to capital

Your virtual cold
chain assistant

SUBMITTED BY

LOCATION

India

PARTNERS

Empa (Swiss Federal Laboratories for Materials Science and Technology)

India is one of the world’s largest food producers, yet 25% to 35% of the food produced is wasted due to a lack of proper refrigeration and other supply chain bottlenecks. Only 6% of the food produced in India currently moves through the cold chain, compared to about 60% in developed countries.

To increase this percentage and support smallholder farmers — who make up the bulk of India’s hungry and poor — BASE & Empa (The Swiss Federal Laboratories for Materials Science and Technology) seek to create an open access, data science-based mobile application, using machine learning and physics-based food modeling. 

Your Virtual Cold-Chain Assistant will enable smallholder farmers to access clean and efficient cooling, easy to access pre- and post- harvest expertise and market intelligence – serviced by another BASE initiative: cooling as a service. With access to these services, smallholder farmer incomes are expected to increase by 10% to 30% per year, while also reducing greenhouse gas emissions & improving food security.

Cities and Towns

Environmental risk
model for revitalization

SUBMITTED BY

PARTNERS

Kansas State University Technical Assistance to Brownfields Program (KSU TAB); Fifth Ward Community Redevelopment Corporation

LOCATION

United States

Rehabilitating underutilized properties in underserved communities — disproportionately home to low-income persons and minorities — can stimulate economic growth and quality of life improvements. However, with the potential high cost and liability for cleanup due to unknown environmental conditions, these properties (known as “brownfields”) can deter investment and exacerbate a neighborhood’s decay. 

Community Lattice proposes incorporating historical brownfields clean-up data in the US with environmental and economic data to create a site cleanup cost model to address the risks associated with brownfield redevelopment. This data and the predictive model would then be available to the public through an online platform. By accurately predicting the cost and risk of brownfield redevelopment projects using machine learning methods, the team seeks to transform a community’s ability to secure redevelopment funding, improve community health, and create economic opportunities.

Jobs of Tomorrow

Use of business intelligence for informal workers

SUBMITTED BY

PARTNERS

UX (Mozambican tech startup); Data Elevates (US social enterprise - data analytics)

LOCATION

Mozambique

In Mozambique, 96% of all workers are employed in the informal sector, facing higher poverty rates, economic insecurity, and fewer opportunities for economic advancement. To increase opportunities for informal workers, UX launched Biscate, a not-for-profit digital job platform where workers can advertise their profession, experience, and location, and potential clients can browse services, contact workers, and leave ratings. 

To increase this platform’s impact, Fundación Capital and UX seek to use data mining, visualization techniques, and a machine learning-powered recommendation system to deliver real-time labor market insights directly to informal workers. This project could have a significant impact on the economic well-being of the vast majority of workers in Mozambique by increasing their job opportunities and potential income.

Cities and Towns

Using data science to
target cash transfers for
COVID-19 relief

SUBMITTED BY

PARTNERS

Data-Intensive Development Lab (DIDL) at UC Berkeley

LOCATION

Togo

On the road to recovery from COVID-19, many countries lack reliable and up-to-date information about economic conditions on the ground and have no way of collecting it during a pandemic. Traditional aid modalities relying on in-person enrollment and delivery are no longer safe or scalable, with governments and NGOs lacking personnel and relief taking weeks to arrive. 

GiveDirectly (GD) and the Center for Effective Global Action at UC Berkeley (CEGA) propose addressing this challenge by developing and testing a new model for humanitarian support that enables cash transfers to be deployed effectively, accurately, and at scale to those who need them most. This project incorporates new data and computational technologies to identify people and places in economic distress and integrating data from mobile phones, satellite imagery, and traditional surveys. The plan will pilot in one or more low and middle-income countries and develop a transparent framework to scale globally.

Access to capital

Empowering women
entrepreneurs with
data science

SUBMITTED BY

PARTNERS

Fraym

LOCATION

Nigeria and Tanzania

Energy poverty is particularly acute in sub-Saharan Africa, where roughly 600 million people lack electricity and 890 million cook with harmful fuels. To address this issue, Solar Sister recruits, trains, and supports local women entrepreneurs to deliver clean energy directly to homes in rural African communities. 

To further support this network of women and expand their social enterprise, Solar Sister is launching Empowering Women Entrepreneurs with Data Science, a collaboration with Fraym. Through this partnership, Fraym will support Solar Sister with data science expertise – using predictive modeling, hyperlocal spatial layers, ensemble-based machine learning pipeline, and covariate matrix to train ML models – to provide insights on potential customers. Insights generated will help women entrepreneurs build tailored customer profiles for off-grid solar, better target their customers, and cater to their needs, growing and sustaining their renewable energy businesses. Leveraging data in this way will build healthy markets for renewable energy solutions by driving innovation and sales, in turn increasing community access to affordable and reliable modern energy, catalyzing socio-economic development, and improving community productivity and livelihood.

Cities and Towns

Mapping and mitigating the urban digital divide

SUBMITTED BY

PARTNERS

KidsFirst Chicago; City Tech Collaborative

LOCATION

United States

Educational and economic opportunities depend on affordable, high-speed Internet access. The COVID-19 pandemic has accelerated and magnified these existing disparities, even within the same city.

The University of Chicago will pilot a project to study fiber connectivity, broadband throughput, application performance metrics, pricing data, and information about subscriptions/affordability to understand communities in Chicago’s access to affordable, high-speed internet. The team will then work with technical and civic partners to design and evaluate new network architectures for improving connectivity in neighborhoods and regions that are sparsely connected. The project’s goal is to create a complete assessment of a city’s broadband infrastructure that can be used to investigate innovative infrastructure solutions, raise awareness about this critical issue, and empower policymakers, industry leaders, and the public to make better and more informed decisions. If successful, this project will have a tremendous impact on Chicago communities and could be scaled to cities and towns across the United States.

Access to Capital

Making data work for women: innovative AI for women’s financial inclusion

SUBMITTED BY

PARTNERS

University of Zurich

LOCATION

India, Mexico, Nigeria

Currently, female entrepreneurs are more likely to get lower premiums, higher interest rates, and increased penalties for mistakes, due to out-of-date, gender biased lending technology and practices. One billion women remain outside the formal financial system today. A solution for this is particularly important in the present climate as emerging markets look to recover from the effects of COVID-19.

To address these issues, Women’s World Banking, in partnership with the University of Zurich, is exploring the implications of AI based modelling and credit scoring on women’s financial inclusion. 

With a strategic focus on two of Women’s World Banking’s key priority markets, researchers will assess how algorithms in digital credit applications can increase lending to women borrowers, study the applications of machine learning and AI, and explore the challenges facing digital financial services as a result of COVID-19.

Paul Ramsay Foundation Award

Jobs of Tomorrow

A fair day’s work: detecting wage theft with data

SUBMITTED BY

LOCATION

Australia

Young workers face an epidemic of underpayment and exploitation – popularly known as wage theft. Young workers are especially vulnerable to wage theft, for reasons that include: a culture of wage theft in industries where young people make up the majority of employees; a lack of awareness of workplace rights; reluctance to complain about exploitation, and lack of resourcing for proactive detection of non-compliance by the regulator. This last point, in turn, makes it difficult for regulators, unions, and other organizations to detect wage theft, let alone address it. Wage theft also impacts business by creating an anti-competitive effect: unscrupulous businesses exploiting workers gain a competitive advantage over businesses who comply with employment laws — which then normalizes wage theft in certain industries.

The University of Melbourne proposes a multi-pronged approach that aims to support young people at risk of wage theft while also providing data for regulators, policymakers, and businesses to drive system change. The project will draw upon cross-disciplinary expertise in labor law and regulation, digital design, information science, UX design, data analysis, and data ethics to design/develop three interlinked components: the Fair Day’s Work portal, a Wage Theft Database, and finally, a Wage Theft Prediction Tool.

See the press release announcing our awardees.