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AI For Social Good (AISG)
Margaux Luck · Kris Sankaran · Tristan Sylvain · Sean McGregor · Jonnie Penn · Girmaw Abebe Tadesse · Virgile Sylvain · Myriam Côté · Lester Mackey · Rayid Ghani · Yoshua Bengio

Sat Jun 15 08:30 AM -- 06:00 PM (PDT) @ 104 B

AI for Social Good

Important information

Contact information: aisg2019.icml.contact@gmail.com

Submission deadline: EXTENDED to April 26th 2019 11:59PM ET

Workshop website

Submission website

Poster Information:

  • Poster Size - 36W x 48H inches or 90 x 122 cm

  • Poster Paper - lightweight paper - not laminated


This workshop builds on our AI for Social Good workshop at NeurIPS 2018 and ICLR 2019.

Introduction: The rapid expansion of AI research presents two clear conundrums:

  • the comparative lack of incentives for researchers to address social impact issues and
  • the dearth of conferences and journals centered around the topic. Researchers motivated to help often find themselves without a clear idea of which fields to delve into.

Goals: Our workshop address both these issues by bringing together machine learning researchers, social impact leaders, stakeholders, policy leaders, and philanthropists to discuss their ideas and applications for social good. To broaden the impact beyond the convening of our workshop, we are partnering with AI Commons to expose accepted projects and papers to the broader community of machine learning researchers and engineers. The projects/research may be at varying degrees of development, from formulation as a data problem to detailed requirements for effective deployment. We hope that this gathering of talent and information will inspire the creation of new approaches and tools by the community, help scientists access the data they need, involve social and policy stakeholders in the framing of machine learning applications, and attract interest from philanthropists invited to the event to make a dent in our shared goals.

Topics: The UN Sustainable Development Goals (SDGs), a set of seventeen objectives whose completion is set to lead to a more equitable, prosperous, and sustainable world. In this light, our main areas of focus are the following: health, education, the protection of democracy, urban planning, assistive technology, agriculture, environmental protection and sustainability, social welfare and justice, developing world. Each of these themes presents unique opportunities for AI to reduce human suffering and allow citizens and democratic institutions to thrive.

Across these topics, we have dual goals: recognizing high-quality work in machine learning motivated by or applied to social applications, and creating meaningful connections between communities dedicated to solving technical and social problems. To this extent, we propose two research tracks:

  • Short Papers Track (Up to four page papers + unlimited pages for citations) for oral and/or poster presentation. The short papers should focus on past and current research work, showcasing actual results and demonstrating beneficial effects on society. We also accept short papers of recently published or submitted journal contributions to give authors the opportunity to present their work and obtain feedback from conference attendees.
  • Problem Introduction Track (Application form, up to five page responses + unlimited pages for citations) which will present a specific solution that will be shared with stakeholders, scientists, and funders. The workshop will provide a suite of questions designed to: (1) estimate the feasibility and impact of the proposed solutions, and (2) estimate the importance of data in their implementation. The application responses should highlight ideas that have not yet been implemented in practice but can lead to real impact. The projects may be at varying degrees of development, from formulation as a data problem to structure for effective deployment. The workshop provides a supportive platform for developing these early-stage or hobby proposals into real projects. This process is designed to foster sharing different points of view ranging from the scientific assessment of feasibility, discussion of practical constraints that may be encountered, and attracting interest from philanthropists invited to the event. Accepted submissions may be promoted to the wider AI solutions community following the workshop via the AI Commons, with whom we are partnering to promote the longer-term development of projects.

Author Information

Margaux Luck (MILA, UdM)
Kris Sankaran (Mila)
Tristan Sylvain (MILA - Universite de Montreal)

I'm a PhD student at MILA, Universite de Montreal.

Sean McGregor (Syntiant)
Jonnie Penn (University of Cambridge)
Girmaw Abebe Tadesse (University of Oxford)
Virgile Sylvain (University of Montreal)
Myriam Côté (Mila)
Lester Mackey (Microsoft Research)
Lester Mackey

Lester Mackey is a machine learning researcher at Microsoft Research, where he develops new tools, models, and theory for large-scale learning tasks driven by applications from healthcare, climate, recommender systems, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and (by courtesy) of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University. He co-organized the second place team in the \$1M. Netflix Prize competition for collaborative filtering, won the \$50K Prise4Life ALS disease progression prediction challenge, won prizes for temperature and precipitation forecasting in the yearlong real-time \$800K Subseasonal Climate Forecast Rodeo, and received a best student paper award at the International Conference on Machine Learning.

Rayid Ghani (University of Chicago)
Yoshua Bengio (Montreal Institute for Learning Algorithms)

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