Harnessing generative AI for impact: Mapping pathways out of poverty (IMP203)

Leveraging Generative AI for Social Impact: FINCA International's Journey with AWS

FINCA International's Mission and Approach

  • FINCA International has been fighting global poverty for 40 years, initially providing small loans to women to invest in micro-businesses.
  • They have learned that lack of access to banking services is only one barrier preventing people from realizing their full potential and getting out of poverty.
  • FINCA has started investing in an emerging class of early-stage social ventures, providing services like clean water, renewable energy, and access to healthcare.
  • Scott Graham's role as a researcher is to provide the evidence that can guide and validate FINCA's work, which presents real challenges in knowing whether these emerging pathways out of poverty are effective and sustainable.

The Research Challenge and the Limitations of Traditional Methods

  • Scott provides a concrete example of FINCA's investment in a company called Meridia, which uses GPS technology to map the perimeters of cocoa farms and help farmers obtain proper land documentation.
  • Evaluating the impact of this intervention is difficult, as land titles are not inherently valuable and their impact is bound up in complex factors like culture, public administration, and behavior.
  • Researchers in the non-profit space typically use a lengthy, manual process to gather and curate relevant source documents, which can take weeks or months.

Automating the Research Process with Generative AI

  • FINCA has developed a proof-of-concept solution called AuRA (Automated Research Assistant), which leverages generative AI to accelerate the research process.
  • AuRA uses a foundational model (Bedrock Anthropic) to expand the initial research query into a topic model, then automates the search for relevant source documents across multiple databases and search engines.
  • The human researcher can then curate the results, provide feedback to the topic model, and regenerate the model using retrieval-augmented generation, resulting in a better-quality impact pathway.

The Importance of a Robust Data Foundation

  • Han Tran emphasizes the importance of a strong data foundation as a prerequisite for any generative AI or AI/ML project, including comprehensive data services, integrated data solutions, and robust data governance.
  • He introduces Amazon Bedrock, a service that allows users to build and scale generative AI applications using large language models and foundation models.

Leveraging Prompt Engineering to Improve Results

  • Han discusses how FINCA used prompt engineering to improve the quality of the titles, topics, and subtopics generated by AuRA, providing more specific instructions and examples to guide the generative AI.
  • Prompt engineering is described as the process of guiding the generative AI to provide the specific answers needed, similar to how one would provide instructions to a human.

The Benefits and Lessons Learned

  • AuRA has provided FINCA's researchers with a curated data library, a Q&A bot to summarize research papers, and the ability to easily navigate and collaborate on the research.
  • This has resulted in a significant increase in research productivity, saving time and effort.
  • Lessons learned include the importance of working backwards from the customer need, quickly involving subject matter experts to validate the LLM outputs, and considering the scalability and cost implications as the solution is expanded.

Next Steps and Opportunities

  • FINCA is exploring ways to improve the reporting layer of AuRA, to better summarize the insights and make the information more accessible to program implementers and decision-makers.
  • This will likely require further prompt engineering and work on the reporting capabilities.
  • The audience is encouraged to explore other AWS Nonprofit initiatives and to contact their AWS account manager if interested in exploring similar generative AI solutions for their organizations.

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