by Muhammad Aurangzeb Ahmad, Hemant Purohit, Oshani Seneviratne
This symposium was centered around the broad vision of how AI could be used for social good through supporting solutions toward the United Nations’ sustainable development goals that touch every aspect of human, social, and economic development. The symposium identified the critical need for responsible AI solutions for these goals, which demand holistic think- ing on optimizing the trade-off between automation benefits and their potential side-effects.
by David Touretzky, Chrisina Gardner-McCune
There has recently been an explosion of activity in K-12 AI education. As the AI4K12 Initiative (ai4k12.org) works to develop national guidelines for teaching AI in US schools, similar efforts have begun in Canada, the U.K., China, and elsewhere. New curricula are being piloted, new tools developed, and new AI electives launched in middle schools and high schools. There have been national and international workshops, and ongoing discussions online about teaching AI in K-12. This symposium brought together the growing community of AI educators, researchers, curriculum designers, and tool developers to discuss the current state of AI education in K-12.
By Kevin Crowston
The Work and AI AAAI symposium was held in Arlington Virginia from 7–9 November 2019. The goal of this symposium was to discuss and plan how AI researchers will contribute to research on human work with artificial intelligence.
By Frank Stein
The AI in Government and Public Sector Applications Symposium was held in Arlington, VA on Nov 7-8, 2019. The goal was to present the status, opportunities and challenges in using AI in government/public sector projects, and to expand the dialog within the community on building, accepting, and using these solutions.
By Florian Buettner, Ulli Waltinger
The symposium on Human-centered AI: Trustworthiness of AI Models & Data was held as part of the AAAI Fall Symposium Series in Arlington, VA on November 7–9, 2019. The focus of the symposium was on AI systems to improve data quality and technical robustness and safety, with additional discussion centered around explainable models, human trust and ethical aspects of AI.