The online interactive magazine of the Association for the Advancement of Artificial Intelligence

AAAI 2019 Fall Symposium Report on AI for Social Good

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.

AAAI 2019 Fall Symposium Report on Teaching AI in K-12

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.

AAAI 2019 Fall Symposium Report on Work and AI

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.

Symposium Report: Human-centered AI: Trustworthiness of AI Models & Data

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.