Earlier this summer, the Computing Community Consortium (CCC) announced a joint white paper competition with Schmidt Futures on the future of “CS for Social Good.” Schmidt Futures, founded by Eric and Wendy Schmidt, is a philanthropic initiative that bets early on people who will make our world better. The goal of the joint white paper competition was to harness computer science (CS) to address societal challenges. CCC put together a review committee consisting of CCC Council members, who read all submitted proposals and made decisions on the final papers. 

The Best Overall Paper was awarded to Connie Moon Sehat from Hacks/Hackers – Credibility Coalition and Ellen Zegura from Georgia Tech for their Misinformation Needs a Data Community: the NewsQA project paper. In this paper, they describe the “News QA” (News Quality Aggregator) project, an initiative of the Tow-Knight Center for Entrepreneurial Journalism, Craig Newmark School of Journalism at CUNY, which is a database that also aims to be the foundation of an ecosystem around the question of news quality. NewsQA aims to provide the raw streams of information with which to understand what combination of factors may be associated with more reliable information or quality journalism in different contexts. 

The Best Paper Addressing Opportunities in AI was awarded to Kush Varshney and Aleksandra Mojsilovic from IBM Research for their Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact paper. In this paper, they discussed how moving from demonstrations to true impact on humanity will require a different course of action, namely open platforms containing foundational AI capabilities to support common needs of multiple organizations working in similar topical areas. They lend credence to this proposal by describing three example patterns of social good problems and their AI-based solutions and argue that the development of such platforms will be possible through convenings of social change organizations, AI companies, and grantmaking foundations.

The Best Paper Authored by Grad Students or Postdocs was awarded to Monica Chan from Teachers College, Columbia University and Ipek Ensari from Columbia University Data Science Institute for their paper entitled Using open-sourced data to nurture a civically engaged and computationally fluent generation. They emphasize the need to educate our next generation to be computationally fluent in data science and analytics, yet be cognizant about the ever-changing climate of the society they live in by staying civically engaged. In addition, Chan and Ensari propose suggestions for interdisciplinary partnerships to make community data available and open-sourced for the purpose of effective, interdisciplinary K-12 data science education, with a curriculum that highlights civic engagement and provides access to all.

Runner up was awarded to Lauren Klein, Beth Smith, Fei Sha, and Maja Matarić all from the University of Southern California for their A Computational Approach to Earlier Detection and Intervention for Infants with Developmental Disabilities paper. They plan to collect a body of data on infant play behaviors with an interactive robotic toy, as well as interactions between the infant and a caregiver with the toy. Using machine learning with data from each infant, they will search for infant behaviors which may be signs of developmental disability. If it works, this will help inform the diagnosis process and enable long term, computationally-enabled interventions to reduce disability and improve motor, cognitive, and social abilities for numerous infants with or at risk for developmental disabilities.

Thank you to everyone who submitted. Congratulations to all the winners!