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AI for social good
Margaux Luck · Tristan Sylvain · Joseph Paul Cohen · Arsene Fansi Tchango · Valentine Goddard · Aurelie Helouis · Yoshua Bengio · Sam Greydanus · Cody Wild · Taras Kucherenko · Arya Farahi · Jonathan Penn · Sean McGregor · Mark Crowley · Abhishek Gupta · Kenny Chen · Myriam Côté · Rediet Abebe

Sat Dec 08 05:00 AM -- 03:30 PM (PST) @ Room 517 B
Event URL: https://aiforsocialgood.github.io/2018/# »

AI for Social Good

Important information

Workshop website

Submission website


The “AI for Social Good” will focus on social problems for which artificial intelligence has the potential to offer meaningful solutions. The problems we chose to focus on are inspired by the United Nations Sustainable Development Goals (SDGs), a set of seventeen objectives that must be addressed in order to bring the world to a more equitable, prosperous, and sustainable path. In particular, we will focus on the following areas: health, education, protecting democracy, urban planning, assistive technology for people with disabilities, agriculture, environmental sustainability, economic inequality, social welfare and justice. Each of these themes present opportunities for AI to meaningfully impact society by reducing human suffering and improving our democracies.

The AI for Social Good workshop divides the in-focus problem areas into thematic blocks of talks, panels, breakout planning sessions, and posters. Particular emphasis is given to celebrating recent achievements in AI solutions, and fostering collaborations for the next generation of solutions for social good.

First, the workshop will feature a series of invited talks and panels on agriculture and environmental protection, education, health and assistive technologies, urban planning and social services. Secondly, it will bring together ML researchers, leaders of social impact, people who see the needs in the field as well as philanthropists in a forum to present and discuss interesting research ideas and applications with the potential to address social issues. Indeed, the rapidly expanding field of AI has the potential to transform many aspects of our lives. However, two main problems arise when attempting to tackle social issues. There are few venues in which to share successes and failures in research at the intersection of AI and social problems, an absence this workshop is designed to address by showcasing these marginalized but impactful works of research. Also, it is difficult to find and evaluate problems to address for researchers with an interest on having a social impact. We hope this will inspire the creation of new tools by the community to tackle these important problems. Also, this workshop promotes the sharing of information about datasets and potential projects which could interest machine learning researchers who want to apply their skills for social good.

The workshop also explores how artificial intelligence can be used to enrich democracy, social welfare, and justice. A focus on these topics will connect researchers to civil society organizations, NGOs, local governments, and other organizations to enable applied AI research for beneficial outcomes. Various case-studies and discussions are introduced around these themes: summary of existing AI for good projects and key issues for the future, AI’s impact on economic inequality, AI approaches to social sciences, and civil society organizations. The definition of what constitutes social good being essential to this workshop, we will have panel discussions with leading social scholars to frame how contemporary AI/ML applications relate to public and philosophical notions of social good. We also aim to define new, quantifiable, and impactful research questions for the AI/ML community. Also, we would like as an outcome of this event the creation of a platform to share data, a pact with leading tech companies to support research staff sabbaticals with social progress organizations, and the connection of researchers to on-the-ground problem owners and funders for social impact.

We invite contributions relating to any of the workshop themes or more broadly any of the UN SDGs. The models or approaches presented do not necessarily need to be of outstanding theoretical novelty, but should demonstrate potential for a strong social impact. We invite two types of submissions. First, we invite research work as short papers (4 page limit) for oral and/or poster presentation. Second, we invite two page abstracts presenting a specific solution that would, if accepted, be discussed during round-table events. The short papers should focus on past and current work, showcasing actual results and ideally demonstrated beneficial effect on society, whereas the two page abstracts could highlight ideas that have not yet been applied in practice. These are designed to foster sharing different points of view ranging from the scientific assessment of feasibility, to discussion of practical constraints that may be encountered when they are deployed, also attracting interest from philanthropists invited to the event. The workshop provides a platform for developing these two page abstracts into real projects with a platform to connect with stakeholders, scientists, and funders.

Author Information

Margaux Luck (MILA)
Tristan Sylvain (MILA)
Joseph Paul Cohen (MILA ShortScience.org)

Joseph Paul Cohen is a researcher and pragmatic engineer. He currently focuses on the challenges in deploying AI tools in medicine specifically computer vision and genomics. He maintains many open source projects including Chester the AI radiology assistant, TorchXRayVision, and BlindTool – a mobile vision aid app. He is the director of the Institute for Reproducible Research, a US non-profit which operates ShortScience.org and Academic Torrents.

Arsene Fansi Tchango (MILA)
Valentine Goddard (Artificial Intelligence Impact Alliance (AIIA))
Aurelie Helouis (MILA)
Yoshua Bengio (Université of Montréal)
Sam Greydanus (Google Brain)

I am a recent graduate of Dartmouth College, where I majored in physics and dabbled in everything else. I have interned at CERN, Microsoft Azure, and the DARPA Explainable AI Project. I like to use memory-based models to generate sequences and policies. So far, I have used them to approximate the Enigma cipher, generate realistic handwriting, and visualize how reinforcement-learning agents play Atari games. One of my priorities as a scientist is to explain my work clearly and make it easy to replicate.

Cody Wild (Sophos Antivirus)
Arya Farahi (University of Michigan - Ann Arbor)
Jonathan Penn (University of Cambridge)

Author, technologist, and historian. Interested in the societal implications of AI over time. PhD candidate in the History and Philosophy of Science Department at the University of Cambridge. Studies the history of AI in the twentieth century. Currently a visiting scholar at MIT. Prior Google Technology Policy Fellow, Assembly Fellow at the MIT Media Lab/Berkman Kline Centre. Holds degrees from the University of Cambridge and McGill University.

Sean McGregor (Syntiant and XPRIZE)
Sean McGregor

Sean McGregor is a machine learning PhD, founder of the Responsible AI Collaborative, lead technical consultant for the IBM Watson AI XPRIZE, and consulting researcher with the neural accelerator startup Syntiant. His current focus is the development of the AI Incident Database as an index of harms or near harms experienced in the real world, which builds on his experience in AI safety and interpretability for deep and reinforcement learning as applied to wildfire suppression policy, speech, and heliophysics. Outside his paid work, Sean's open source development work has earned media attention in the Atlantic, Der Spiegel, Mashable, Wired, Venture Beat, Vice, and O'Reilly while his technical publications have appeared in a variety of machine learning, HCI, ethics, and application-centered proceedings.

Mark Crowley (University of Waterloo)
Mark Crowley

Prof. Mark Crowley runs the UWECEML lab and is an Associate Professor at the University of Waterloo in the ECE department. His research explores how to augment human decision making in complex domains in dependable and transparent ways by investigating the theoretical and practical challenges that arise from the presence of spatial structure, large scale streaming data, uncertainty, or unknown causal structure, or interaction of multiple decision makers. His focus is on developing new algorithms, methodologies, simulations, and datasets within the fields of Reinforcement Learning (RL), Deep Learning, Manifold Learning and Ensemble Methods.

Abhishek Gupta (Montreal AI Ethics Institute, Microsoft, and McGill University)
Kenny Chen (Ascender)
Myriam Côté (MILA, Institut québécois d'intelligence artificielle)
Rediet Abebe (Cornell University)

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