The seminar is entitled, "Harnessing Human and Artificial Collective Intelligence for Open-Ended Decision Making in Development", via Zoom. In order to attend the seminar, please follow this link.
Jaron Porciello is an Information Scientist and a Senior Extension Associate in Global Development and International Agriculture at Cornell University. Jaron’s research focuses building sociotechnical programs that use artificial intelligence, mainly interpretable machine learning and its applications, to help people make better decisions. As part of her research portfolio, she leads the evidence network for Ceres2030: Sustainable Solutions to End Hunger. Jaron Porciello works with a range of international organizations, funders, and publishers and has significant experience developing and designing computational tools and managing projects for low-bandwidth environments across low- and middle-income countries, primarily with a focus on countries in sub-Saharan Africa and South Asia.
Abstract: Governments and international funders supporting the Sustainable Development Goals recognize the importance that sound science and evidence are key to underpinning policy. Solutions to development problems are often rooted in domain-specific knowledge areas such as agriculture and livelihoods, environment and natural resource management, nutrition and health, and human capital and education. Yet the digital and data divide between wealthy and poorer countries looms large, and proposed solutions are often designed in a time or for a context that may look very different from the world in which they will be ultimately deployed, giving way to unintended, unforeseen consequences especially in low- and middle-income countries.
Expert knowledge is key as decisions need to be taken by integrating multiple information sources, incorporating accumulated experience, and weighing uncertainty. At the same time, the amount of available evidence is exponentially increasing which makes it difficult to provide evidence-based interventions while avoiding the risk of confirmation bias or cherry-picking.
Novel hybrid collective intelligence can provide the decision support needed to help make sense of information in a way that addresses complex, open-ended problems, promoting engagement, fairness, and trust. In her talk, Porciello will describe her programs and experience leading real-world evidence-based initiatives where interpretable machine-learning tools and platforms have supported researchers, funders, and governments prioritize investment decisions in the international effort to end hunger.