AI for Social Good
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.
Margaux Luck (MILA)
Tristan Sylvain (MILA)
Joseph Paul Cohen (MILA ShortScience.org)
!(https://i.imgur.com/FQwXTGR.png) [ShortScience.org profile](http://www.shortscience.org/user?name=joecohen) Joseph Paul Cohen holds a Ph.D Degree in Computer Science and Machine Learning from the University of Massachusetts Boston. His research interests include machine learning, domain adaptation, computer vision, medical applications, ad-hoc networking, and cyber security. Joseph received a U.S. National Science Foundation Graduate Fellowship in 2013 as well as COSPAR’s Outstanding Paper Award for Young Scientists in the same year. Joseph is the founder of the Institute for Reproducible Research which produces [ShortScience.org](http://shortscience.org); which lets researchers publish and read summaries of research papers like an online journal club, as well as [Academic Torrents](http://academictorrents.com); a system designed to move large datasets and become the library of the future. He is also the creator of BlindTool; a mobile application providing a sense of vision to the blind by using an artificial neural network that speaks names of objects as they are identified. Joseph is the creator of Blucat; a cross-platform Bluetooth debugging tool. He has worked in industry for small startups, large corporations, government research labs, educational museums, as well as been involved in projects sponsored by NASA and the DOE.
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)
Taras Kucherenko (KTH ROYAL INSTITUTE OF TECHNOLOGY)
Arya Farahi (University of Michigan - Ann Arbor)
Jonnie 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 defended a PhD at Oregon State University in 2017. His research focuses on solving real world problems with machine learning and visual analytics, including problems in wildfire suppression, heliophysics, and analog neural network computation. Outside his research, Sean serves as technical lead for the IBM Watson AI XPRIZE and representative to the Partnership on AI for the Safety Critical AI and Fair, Transparent, and Accountable (FTA) working groups. Sean's "day job" is developing neural networks to run on analog architectures at Syntiant.
Mark Crowley (University of Waterloo)
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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