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AI for Social Good
Important information
Abstract
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.
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Sat 5:30 a.m. - 5:45 a.m.
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Opening remarks
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Opening remarks by Yoshua Bengio. |
Yoshua Bengio 🔗 |
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Sat 5:45 a.m. - 6:15 a.m.
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AI for agriculture, environmental protection and sustainability
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Invited talk
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link »
A key challenge in Africa is the lack of sufficient domain experts to effectively solve the problems in health, agriculture, education, transport, etc. Artificial Intelligence with all the current advances provides hope for a solution by enabling the automation of these varied expert tasks. Africa is largely an agro-economy where majority of the people depend on agriculture for their livelihood. In this talk I will give some examples of interventions in AI being employed by the AI and Data Science research lab (AIR) in Makerere University and the UN Pulse Lab to address some of these challenges. I will talk about automating disease diagnosis in the field on smartphones and crowdsourcing surveillance data from farmers with smartphones and how these impact the livelihoods of these farmers. I will also give some examples in other fields like health where AI is impacting the livelihoods of people in Africa. |
Ernest T Mwebaze 🔗 |
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Sat 6:15 a.m. - 6:45 a.m.
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How AI can empower the blind community
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Invited talk
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link »
In this demo based talk, Anirudh will discuss the real-life impact that AI is already bringing in the daily lives of the blind and low vision community. Learning from failures and success while converting research to product, the talk showcases a range of real-world scenarios which can benefit from both classical computer vision as well as deep learning based techniques. Separating hype from reality, it also highlights open opportunities for innovation where many traditional datasets & benchmarks do not convert to in-the-wild usage beyond fancy demos. Deep learning techniques can also help improve human-computer interaction, which might be the key to making these advances usable. The key underlying theme to recognize is how developing for differently abled communities can lead to innovation for mainstream audiences. |
Anirudh Koul 🔗 |
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Sat 6:45 a.m. - 7:00 a.m.
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Rural Infrastructure Health Monitoring System: Using AI to Increase Rural Water Supply Reliability ( Contributed talk ) link » | Girmaw Abebe Tadesse 🔗 |
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Sat 7:00 a.m. - 7:15 a.m.
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Exploiting data and human knowledge for predicting wildlife poaching ( Contributed talk ) link » | Fei Fang 🔗 |
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Sat 7:15 a.m. - 7:30 a.m.
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Inferring Work Task Automatability from AI Expert Evidence
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Contributed talk
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Logan Graham 🔗 |
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Sat 7:30 a.m. - 8:00 a.m.
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Poster session
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Poster session / Coffee break
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Ralf Mayet · Paulo Orenstein · Heloise Greeff · Tomasz Rutkowski · Jiafan Yu · Milena Marin · Peter He · Jigar Doshi · Xavier Boix · Thomas Janssoone · Aniket Kesari · Yunyi Li · Arbel Vigodny · Ellie Gordon · Zach Moshe · Sella Nevo · Harvey Wu · Jessica Lee · Noel Corriveau · Vincenzo Lomonaco · Yada Pruksachatkun · Naroa Zurutuza · Bhairav Mehta · Carolyne Pelletier · Yasmeen Hitti · Sophia Latessa · Gerard Glowacki · Alexis G Gkantiragas · Oliver Nina · Íñigo Martínez de Rituerto de Troya · Vedran Sekara · Michael Madaio · Eunbee Jang · Ines Moreno · Arnon Houri-Yafin · Claire Babirye
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Sat 8:00 a.m. - 9:00 a.m.
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AI’s impact on economic inequality, class
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Panel
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John Havens · Ifeoma Ajunwa 🔗 |
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Sat 9:00 a.m. - 10:00 a.m.
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AI's Impact on Art, Music, and Culture, featuring Yo-Yo Ma, Element AI, and Special Guests
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Demonstration
)
link »
This special program features a demo, two short presentations, and panel discussion, hosted over lunch provided by Element AI. First, renowned cellist Yo-Yo Ma will perform in a live demonstration of music and neuroinformatics, using an Emotiv wireless EEG headset operated by Emotiv President, Olivier Oullier. Next, Valérie Bécaert, Director of Research and Scientific Programs at Element AI, will present on arts and AI initiatives in Montreal, as well as a new collaborative residency launched. Additionally, Karina Kesserwan of Kesserwan Arteau will discuss her firm's work on issues relating to Indigenous and Northern communities. To conclude, all presenters will engage in a panel discussion regarding the impact of artificial intelligence on such domains as art, music, and culture. |
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Sat 10:00 a.m. - 11:00 a.m.
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Bias and fairness in AI
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Panel
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Timnit Gebru · Margaret Mitchell · Brittny-Jade E Saunders 🔗 |
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Sat 11:00 a.m. - 11:15 a.m.
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Anti-Malaria Operations Planning and Management
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Use-cases
)
link »
Despite malaria’s tremendous impact on public health and economic development in Africa, and a 3 billion USD annual investment in its control, the prospect of a malaria-free Africa seems distant. In this session, I will discuss how Zzapp is harnessing AI and mobile technologies to address the key challenges to malaria elimination today. Through the use of deep learning algorithms, we are developing a planning tool that will design ever-improving intervention strategies, customized to individual urban and rural communities. To simplify the implementation of these strategies, and to ensure effective execution, we have launched a mobile app that relays instructions directly to workers in the field, and monitors them using GPS. In collaboration with several research institutes and malaria control programs in Africa, we adapt these tools to the current and future needs of malaria elimination campaigns. |
Arbel Vigodny 🔗 |
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Sat 11:15 a.m. - 11:30 a.m.
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A Wearable, Biomarker-Tracking Device Platform Using Machine Learning to Predict and Prevent Opioid Relapses and Overdoses in Real Time
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Use-cases
)
link »
For individuals with substance use disorder (SUD), the propensity for returning to drug use (relapsing) is high, topping 90 percent for heroin users. The current methods and tools to counter drug addiction have been inefficient, resulting in frequent relapses and increasing overdose rates. Historically, these tools have been retrospective, with the intervention happening far too late and with a lack of focus on eliminating trigger scenarios. The goal at Behaivior is to refocus the recovery process onto the status of the individual before relapse occurs, observing both internal and external factors that affect craving states in real time. |
Ellie Gordon 🔗 |
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Sat 11:30 a.m. - 11:45 a.m.
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Machine Learning-Based Screening for Fetal Alcohol Spectrum Disorder ( Contributed talk ) link » | Chen Zhang 🔗 |
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Sat 11:45 a.m. - 12:00 p.m.
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Enabling better pregnancy monitoring: The case of point-of-care diagnosis in fetal echocardiography ( Contributed talk ) link » | Arijit Patra 🔗 |
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Sat 12:00 p.m. - 12:30 p.m.
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Poster Session ( Poster session / Coffee break ) link » | 🔗 |
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Sat 12:30 p.m. - 1:30 p.m.
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The role of civil society in the age of AI: Beyond buzzwords
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Panel
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Kathleen Siminyu · Milind Tambe · Michael Skirpan · Dongwoo Kim 🔗 |
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Sat 1:30 p.m. - 1:45 p.m.
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Improving Traffic Safety Through Video Analysis in Jakarta, Indonesia ( Contributed talk ) link » | 🔗 |
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Sat 1:45 p.m. - 2:00 p.m.
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Inverse Optimal Power Flow: Assessing the Vulnerability of Power Grid Data ( Contributed talk ) link » | Priya Donti 🔗 |
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Sat 2:00 p.m. - 3:00 p.m.
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Academia, Corporations, Society, Responsibility ( Panel ) link » | David Danks · Julien Cornebise · Lisa Di Jorio 🔗 |
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)
Taras Kucherenko (KTH ROYAL INSTITUTE OF TECHNOLOGY)
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 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)
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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