Artificial Intelligence for Development
Papers from the AAAI Spring Symposium
Nathan Eagle and Eric Horvitz, Cochairs
The AAAI Spring Symposium on Artificial Intelligence for Development (AI-D) explored opportunities for harnessing AI to promote the socioeconomic development and enhance the quality of life of disadvantaged populations, including those within developing countries. Beyond discussing and sharing ideas, the purpose of the symposium was to catalyze the creation of AI-D as a subfield of the more established broader area of information and communication technology for development (ICT-D).
Machine learning, inference, planning, and perception have the potential to bring great value to the developing world in a wide array of areas, including healthcare, education, transportation, and agriculture. AI methods promise to provide new directions for enhancing and extending novel economic concepts like microfinance and microwork. The methods can be used to assist with the detecting and responding to natural and human-caused disasters. Reasoning systems might one day help to extend medical care to remote regions through automated diagnosis and effective triaging of limited medical expertise and transportation resources. Unprecedented quantities of data are being generated in the developing world on human health, financial transactions, movement, and communications. AI methods can help to tease out insights from this data on social relationships and dynamics, human mobility patterns, and population responses to crises. Models and systems that leverage such data might one day guide public policy, monitor interventions, and provide insights about population responses to crises.
To date, ICT-D efforts have led to valuable ideas and insights. However, ICT-D efforts have rarely focused on opportunities to harness machine learning and reasoning to create intelligent systems, services, models, and analyses. This AAAI Spring Symposium at Stanford served as a focal point and launching pad for bringing together a critical mass of researchers who share an interest in applying AI research to development challenges.