As social impact organizations accelerate digitization, they are increasingly aware of the untapped potential lying within their data, and how AI solutions leveraging this data can amplify impact. With ethical guardrails as a core consideration, pioneering organizations are blazing the trail for AI for social impact across the globe.
The IBM Data Science and AI Elite (DSE) Team spearheaded an incubator to facilitate exploration and use of AI for social impact organizations. Social impact organizations worked alongside IBM DSE and IBM Research experts, supported by IBM Volunteers through the Academy of Technology. The incubator included direct product feedback that informs IBM’s approach to developing products that are relevant to diverse audiences in an effort to narrow the data divide.
Four organizations hand-picked
Amongst a competitive applicant pool, the team selected four organizations:
◉ Center for Court Innovation (CCI) and Alabama Appleseed teamed with IBM to analyze social disparities in the criminal system of the state of Alabama using AI and Data Science tools. CCI is a non-profit organization focused on creating operating programs to test new ideas, solve problems, and provide expert assistance to justice reformers around the world. Alabama Appleseed is a public policy and direct service organization based in Montgomery and Birmingham that uses policy analysis, original research, public education, and community organizing to build a more just and equitable Alabama. Using Watson Studio and the AI Fairness 360 toolkit, IBM data scientists analyzed court debt data to identify factors that have a detrimental impact on people’s lives and use its outcomes to potentially impact relevant state-wide policies. The models identified the most vulnerable sub-populations facing disproportionate consequences in the criminal system and have enabled research scientists from CCI and Alabama Appleseed to continue to study fairness at the individual and population level.
“At a time when AI is rarely used within non-profit and governmental sectors, collaborating with the team on an AI project was eye opening,” said Andrew Martinez, Principal Research Scientist with the Center for Court Innovation, “I walk away inspired to continue to explore ways to leverage AI capabilities into our work. “
◉ Greater DC Diaper Bank (GDCDB) is the largest diaper bank in the DC Metro region. It works with over 70 social service organizations to distribute over 700,000 diapers and other essential hygiene products to low-income families each month. GDCDB combined their own geo-based data on diaper distribution with external data related to various indicators of need and support to create a more comprehensive and hyper-local understanding of diaper need by zip code in the DC Metro Area. To attain data-driven insights, GDCDB worked with IBM to analyze the data, estimate need, and create an interactive map to visualize potential need. The team used machine learning to identify groups of communities that experience similar challenges and where similar approaches to diaper distribution might work. The tool may also eventually allow GDCDB staff to proactively predict future diaper need in specific areas in order to better serve families and work with community leaders to collectively end diaper need in the DC region, using Watson Studio, IBM Cognos, and IBM SPSS Modeler Flows.
“For years we’ve been talking about leveraging public data to help us better identify diaper need in the region, and to also help us identify solutions for how to best meet that need,” said Carrie Fassett, Director of Partnerships and Impact at GDCDB “The IBM incubator helped make that vision a reality for us.”
◉ Neighborhood Trust Financial Partners is a financial services innovator that creates financial security for low-wage workers through workplace and marketplace solutions. IBM and Neighborhood Trust teams collaborated to explore and analyze data to derive insights and build a machine learning pipeline to understand the relationship between users’ financial characteristics (such as bank transactions and credit reports) and their probability of experiencing financial distress. The team prioritized transparency to explain the impact of features on user’s likelihood of being in financial distress. Part of the solution relied around the application of a novel segmentation algorithm called ProtoDash, which allowed the team to identify “prototypical users”, or users that are the most representative of Neighborhood Trusts’ user base. The team used Watson Studio, AutoAI, and the AI Explainability 360 toolkit during their engagement.
Leo Rayfiel, Associate Director of Data & Analytics at Neighborhood Trust Financial Partners, detailed his experience, “Our collaboration with IBM significantly upgraded our organization’s ability to conduct advanced analytics projects. We finished our project with strong deliverables as well as a set of clearly explained and easy-to-use tools that will enable us to independently iterate on the work.”
Ready, set, build
The incubator featured a launch program with educational sessions including talks from partners including Change Machine and Urban Institute, along with several executive speakers who shared best practices for application of data science and AI. The organizations then began incubation projects alongside the DSE team, building models and data capacity alike. The cohort had the benefit of learning not only from their project experience throughout the program, but also from each other’s experiences.
Aakanksha Joshi, Lead Data Scientist, IBM DSE shares “It is an incredible experience to work with organizations in the non-profit sector, each with their own unique set of data and AI challenges. The organizations came together to share lessons learned across data acquisition, data selection, data preprocessing, and application of concepts like fairness, explainability and agility in the data science and machine learning lifecycle.”
Discover how other organizations are using IBM Cloud Pak® for Data to drive impact in their business and the world.
Source: ibm.com
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