AI for Social Good workshop

Date: 

Mon - Tue, Jul 20 to Jul 21, 9:00am - 4:00pm

Location: 

This workshop is entirely virtual

Jon us on July 20–21 at the AI for Social Good workshop. This event will explore how artificial intelligence can contribute to solving social problems and will bring together researchers and practitioners across artificial intelligence and a range of application domains.

Accepted Papers | Call for Papers | Invited Speakers | Organization | Program | Travel Awards

Artificial intelligence is poised to play an increasingly large role in societies across the world. Accordingly, there is a growing interest in ensuring that AI is used in a responsible and beneficial manner. A range of perspectives and contributions are needed, spanning the full spectrum from fundamental research to sustained deployments.

This workshop will explore how artificial intelligence can contribute to solving social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can AI initiatives be deployed in an ethical, inclusive, and accountable manner? To address such questions, this workshop will bring together researchers and practitioners across artificial intelligence and a range of application domains. The objective is to share the current state of research and practice, explore directions for future work, and create opportunities for collaboration.

Such questions are particularly salient in light of the COVID-19 pandemic. AI has an important role to play in providing insight into the course of the epidemic and developing targeted responses; we encourage submissions from both AI researchers as well as epidemiologists, health policy researchers, and other domain experts who are interested in engaging with the AI community.

Accepted Papers

Lynn Miller, Christoph Rüdiger and Geoffrey Webb. Using AI and Satellite Earth Observation to Monitor UN Sustainable Development Indicators.

Zhigang Zhu, Jie Gong, Cecilia Feeley, Huy Vo, Hao Tang, Arber Ruci, Bill Seiple and Zheng Yi Wu. SAT-Hub: Smart and Accessible Transportation Hub for Assistive Navigation and Facility Management.

Taoan Huang and Bistra Dilkina. Enhancing Seismic Resilience of Water Pipe Networks.

Xingyu Chen and Zijie Liu. The Fairness of Leximin in Allocation of Indivisible Chores.

Deepak P. Whither Fair Clustering?.

Satyam Mohla, Sidharth Mohla and Anupam Guha. Green is the new Black: Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest.

Anusua Trivedi, Kate Keator, Avirishu Verma, Rahul Dodhia, Ria Sankar and Juan Lavista Ferres. ECO: Using AI for Everyday Armed Conflict Analysis.

Kim de Bie, Nishant Kishore, Anthony Rentsch, Pablo Rosado and Andrea Sipka. Using AI to help healthcare professionals stay up-to-date with medical research.

Subhro Das, Sebastian Steffen, Prabhat Reddy, Erik Brynjolfsson and Martin Fleming. Forecasting Task-Shares and Characterizing Occupational Change across Industry Sectors.

Athina Georgara, Carles Sierra and Juan-Antonio Rodríguez-Aguilar. Edu2Com: an anytime algorithm to form student teams in companies..

Jennifer Hobbs, Robert Paull, Bernard Markowicz and Greg Rose. Flowering density estimation from aerial imagery for automated pineapple flower counting.

Melanie Laffin. Ethically Sourced Modeling: A Framework for Mitigating Bias in AI Projects within the US Government.

Golnoosh Farnadi, Behrouz Babaki and Margarida Carvalho. Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration/publications/fairness-kidney-exchange-programs-through-optimal-solutions-enumeration.

Harshit Mishra. Reducing Word Embedding Bias Using Learned Latent Structure.

Wanyi Li, Nicole Immorlica and Brendan Lucier. Contract Design for Afforestation Programs.

Alexandra Luccioni, Joseph Bullock, Katherine Hoffmann Pham, Cynthia Sin Nga Lam and Miguel Luengo-Oroz. Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19.

Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K Patro, Manish Raghavan, Ana-Andreea Stoica and Stratis Tsirtsis. Fairness and Discrimination in Mechanism Design and Machine Learning.

Robin Burke, Amy Voida, Nicholas Mattei and Nasim Sonboli. Algorithmic Fairness, Institutional Logics, and Social Choice.

Tine Kolenik and Matjaž Gams. Progressing Social Good by Reducing Mental Health Care Inequality with Persuasive Technology.

Caroline Trier and Lu Sevier. Designing a Partnership Framework in AI for Social Good.

Yassir Alharbi, Daniel Arribas-Be and Frans Coenen. Sustainable Development Goal Relational Modelling: Introducing the SDG-RMF Methodology.

Sagar Vaze, Conrad James Foley, Mohamed Seddiq, Alexey Unagaev and Natalia Efremova. Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks.

Nawal Benabbou, Mithun Chakraborty, Ayumi Igarashi and Yair Zick. Finding Fair and Efficient Allocations When Valuations Don’t Add Up.

Sean R. Sinclair, Gauri Jain, Siddhartha Banerjee and Christina Lee Yu. Sequential Fair Allocation of Limited Resources under Stochastic Demands.

Lance Eliot. Position Paper: The Neglected Dualism Of Artificial Moral Agency And Artificial Legal Reasoning In AI For Social Good.

Christian Kammler, Annet Onnes, Loïs Vanhée, Harko Verhagen, Bart de Bruin, Paul Davidsson, Frank Dignum, Virginia Dignum, Amineh Ghorbani, Mijke van den Hurk, Maarten Jensen, Kurt Kreulen, Fabian Lorig, Luis Gustavo Ludescher, Alexander Melchior, René Mellema, Cezara Pastrav and Tomas Sjöström. Social Simulations for Intelligently Beating COVID-19.

Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang and Milind Tambe. Dual-Mandate Patrols: Bandit-Based Learning in Green Security Domains.

Raushan Raj, Anand Seetharam and Arti Ramesh. Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases.

Swaroop Gowdra, Anand Seetharam and Arti Ramesh. Understanding the Socio-Economic Disruption in the United States during COVID-19’s Early Days.

Bob Bell and Rajesh Veeeraraghavan. Locating Informal Urban Settlements.

Harold Kiossou, Yannik Schenk, Frédéric Docquier, Ratheil Houndji, Siegfried Nijssen and Pierre Schaus. Using an interpretable Machine Learning approach to study the drivers of International Migration.

Arjun Verma and Vikram Sarbajna. A deep learning based approach for monitoring sustainable farming practices at a parcel level.

Tom Ron, Omer Ben-Porat and Uri Shalit. Corporate Social Responsibility via Multi-Armed Bandits.

Jawad Haqbeen, Takayuki Ito, Sofia Sahab, Rafik Hadfi, Shun Okuhara, Nasim Saba, Murtaza Hofiani and Umar Baregzai. A Contribution to COVID-19 Prevention through Crowd Collaboration using Con-versational AI & Social Platforms.

Sai Venkata Ratna Rithwik Kukunuri, Nipun Batra, Ayush Pandey, Raktim Malakar, Rajat Kumar, Odysseas Krystalakos, Mingjun Zhong, Paulo Meira and Oliver Parson. NILMTK-Contrib: Towards reproducible state-of-the-art energy disaggregation.

Diana Diaz, Celia Cintas, William Ogallo and Aisha Walcott-Bryant. Towards Automatic Generation of Context-Based Abstractive Discharge Summaries for Supporting Transition of Care.

Chao Yan, Haifeng Xu, Yevgeniy Vorobeychik, Bo Li, Daniel Fabbri and Bradley Malin. To Warn or Not to Warn: Online Signaling in Audit Games.

Swati Padhee, Tanay Kumar Saha, Joel Tetreault and Alejandro Jaimes. Clustering of Social Media Messages for Humanitarian Aid Response during Crisis.

Guillaume Derval, Vincent François-Lavet and Pierre Schaus. Nowcasting COVID-19 hospitalizations using Google Trends and LSTM.

Caroline Johnston, Simon Blessenohl and Phebe Vayanos. Preference Elicitation and Aggregation to Aid with Patient Triage during the COVID-19 Pandemic.

Yan Zhou and Murat Kantarcioglu. On Transparency of Machine Learning Models: A Position Paper.

Lily Xu, Andrew Perrault, Andrew Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba and Milind Tambe. Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers.

Bryan Wilder, Marie Charpingon, Jackson Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe and Maimuna Majumder. Inferring between-population differences in COVID-19 dynamics.

Ezinne Nwankwo, Chinasa Okolo and Cynthia Habonimana. Topic Modeling Approaches for Understanding COVID-19 MisinformationSpread in Sub-Saharan Africa.

Anya Zhang and Andrew Perrault. Influence Maximization and Equilibrium Strategies in Election Network Games.

Ilkin Bayramli, Elizabeth Bondi and Milind Tambe. In the Shadow of Disaster: Finding Shadows to Improve Damage Detection.

Brian Brubach, Aravind Srinivasan and Shawn Zhao. The Relationship between Gerrymandering Classification and Voter Incentives.

Ashiqur Khudabukhsh, Shriphani Palakodety and Jaime Carbonell. On NLP Methods Robust to Noisy Indian Social Media Data.

Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar, Dhyanesh Narayanan, Anirudh Grama, Aparna Hegde, Ramesh Padmanabhan, Neha Madhiwalla, Suresh Chaudhary, Balaraman Ravindran and Milind Tambe. Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement.

Susan Leavy, Barry O’Sullivan and Eugenia Siapera. Data, Power and Bias in Artificial Intelligence.

Jakob Jonnerby, Philip Lazos, Edwin Lock, Francisco Marmolejo-Cossío, Christopher Bronk Ramsey and Divya Sridhar. Test and Contain: A Resource-Optimal Testing Strategy for COVID-19.

Alaisha Sharma, Jackson Killian and Andrew Perrault. Optimization of the Low-Carbon Energy Transition Under Static and Adaptive Carbon Taxes via Markov Decision Processes.

Call for Papers

The Harvard CRCS Workshop on AI for Social Good will explore how artificial intelligence can contribute to solving social problems. For example, what role can AI play in promoting health, access to opportunity, and sustainable development? How can AI initiatives be deployed in an ethical, inclusive, and accountable manner? To address such questions, this workshop will bring together researchers and practitioners across artificial intelligence and a range of application domains. The objective is to share the current state of research and practice, explore directions for future work, and create opportunities for collaboration. The workshop will feature a mix of invited talks, contributed talks, and posters. Submissions spanning the full range of theoretical and applied work are encouraged. Topics of interest include, but are not limited to:

  • Democracy
  • Developing world
  • Health
  • Environmental sustainability
  • Ethics
  • Fairness and biases

Such questions are particularly salient in light of the COVID-19 pandemic. AI has an important role to play in providing insight into the course of the epidemic and developing targeted responses; we encourage submissions from both AI researchers as well as epidemiologists, health policy researchers, and other domain experts who are interested in engaging with the AI community.

Submissions are due June 5, AoE, via EasyChair. We solicit papers in two categories:

  • Research papers describing novel contributions in either the development of AI techniques (motivated by societal applications), or their deployment in practice. Both work in progress and recently published work will be considered. Submissions describing recently published work should clearly indicate the earlier venue and provide a link to the published paper. Papers in this category should be at most 4 pages, with unlimited additional pages containing only references.

  • Position papers describing open problems or neglected perspectives on the field, proposing ideas for bringing computational methods into a new application area, or summarizing the focus areas of a group working on AI for social good. Papers in this category should be at most 3 pages, with unlimited additional pages containing only references.

All papers should be submitted in IJCAI format. The workshop will not have a formal published proceedings, but we will provide links to accepted papers along with the program. Accepted papers will be selected for oral and poster presentation based on peer review. Submissions are not double-blind; the submitted paper should include author names and affiliations.

Invited Speakers

Joanna Bryson (Hertie School of Governance)

What Is Good? Social Impacts and Digital Governance

Bio: Joanna Bryson is Professor of Ethics and Technology at Hertie School of Governance in Berlin recognised for broad expertise on intelligence, its nature, and its consequences. She advises governments, transnational agencies, and NGOs globally, particularly in AI policy. Most recently, she has been selected to represent Germany at the Global Partnership on AI. She holds two degrees each in psychology and AI (BA Chicago, MSc & MPhil Edinburgh, PhD MIT). Her work has appeared in venues ranging from reddit to the journal Science. From 2002-19 she was Computer Science faculty at the University of Bath; she has also been affiliated with Harvard Psychology, Oxford Anthropology, The Mannheim Centre for Social Science Research, The Konrad Lorenz Institute for Evolution and Cognition Research, and the Princeton Center for Information Technology Policy. During her PhD she first observed the confusion generated by anthropomorphised AI, leading to her first AI ethics publication “Just Another Artifact” in 1998. She has remained active in the field including coauthoring the first national-level AI ethics policy, the UK’s (2011) Principles of Robotics. She continues to research both the systems engineering of AI and the cognitive science of intelligence, with present focusses on the impact of technology on human cooperation, and new models of governance for AI and ICT.

Oisin Mac Aodha (School of Informatics, University of Edinburgh)

Human-in-the-Loop Computer Vision for Biodiversity Monitoring

Abstract: In order to manage the impact of anthropogenic change there is a critical need for robust and accurate tools to scale up biodiversity monitoring. Machine learning powered systems offer the promise of scaling up current manually intensive monitoring by automatically processing large quantities of image and audio data to find events of interest e.g. detecting the presence of a particular species. In this talk I will discuss recent work where we have shown that by explicitly including humans-in-the-loop we can improve the accuracy of these systems. I will touch on recent computer vision benchmarks for fine-grained visual understanding, algorithms for teaching visual knowledge to humans, and models for exploiting spatial and temporal biases in data to improve image classification performance.

Bio: Oisin Mac Aodha is an Assistant Professor in Machine Learning at the University of Edinburgh. Previous to that, he was a postdoc with Prof. Pietro Perona in the Computational Vision Lab at the Caltech. He obtained his PhD from University College London with Prof. Gabriel Brostow. His current research interests are broadly in the areas of machine learning, computer vision, and human-in-the-loop methods such as active learning and machine teaching. More information can be found on his website (www.oisin.info) and twitter (@oisinmacaodha).

Vukosi Marivate (Department of Computer Science, University of Pretoria)

Data Science at the frontier: What does it really mean to do Data Science for Society?

Abstract: We talk about Data Science or Artificial Intelligence for Good. What does it really mean to do this? What are the challenges when you have society in the loop of our science? Actually, what does it mean to center society in this science? I go through trying answer these questions and use some of our recent work on COVID-19 in South Africa and African Natural Language processing to better understand what we have learned through his process.

Bio Dr Vukosi Marivate is the ABSA UP Chair of Data Science at the University of Pretoria. Vukosi works on developing Machine Learning/Artificial Intelligence methods to extract insights from data. A large part of his work over the last few years has been in the intersection of Machine Learning and Natural Language Processing (due to the abundance of text data and need to extract insights). As part of his vision for the ABSA Data Science chair, Vukosi is interested in Data Science for Social Impact, using local challenges as a springboard for research. In this area Vukosi has worked on projects in science, energy, public safety and utilities. Vukosi is an organiser of the Deep Learning Indaba, the largest Machine Learning/Artificial Intelligence workshop on the African continent, aiming to strengthen African Machine Learning. He is passionate about developing young talent, supervising MSc and PhD students and mentoring budding Data Scientists.

Ziad Obermeyer (UC Berkeley School of Public Health)

Dissecting racial bias in health algorithms

Abstract: The choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts. We illustrate this with an empirical example from health, where commercial prediction algorithms are used to identify and help patients with complex health needs. We show a widely-used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: at a given risk score, blacks are considerably sicker than whites, as evidenced by signs of uncontrolled illnesses. Remedying this would increase blacks receiving additional help from 17.7% to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness. But unequal access to care means we spend less caring for blacks than whites. So, despite appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise.

Bio: Ziad Obermeyer is an Associate Professor at UC Berkeley, where he does research at the intersection of machine learning, medicine, and health policy. He was named an Emerging Leader by the National Academy of Medicine, and has received numerous awards including the Early Independence Award – the National Institutes of Health’s most prestigious award for exceptional junior scientists – and the Young Investigator Award from the Society for Academic Emergency Medicine. Previously, he was an Assistant Professor at Harvard Medical School. He continues to practice emergency medicine in underserved communities.

Danielle Wood (MIT Media Lab)

Sustainability in Space and on Earth: Research Initiatives of the Space Enabled Research Group

Abstract: The presentation will present the work of the Space Enabled Research Group at the MIT Media Lab. The mission of the Space Enabled Research Group is to advance justice in Earth’s complex systems using designs enabled by space. Our message is that six types of space technology are supporting societal needs, as defined by the United Nations Sustainable Development Goals. These six technologies include satellite earth observation, satellite communication, satellite positioning, microgravity research, technology transfer, and the infrastructure related to space research and education. While much good work has been done, barriers remain that limit the application of space technology as a tool for sustainable development. The Space Enabled Research Group works to increase the opportunities to apply space technology in support of the Sustainable Development Goals and to support space sustainability. Our research applies six methods, including design thinking, art, social science, complex systems, satellite engineering and data science. The presentation will give examples of research projects that harness tools from artificial intelligence, satellite data analysis and machine learning. The projects build relationships with leaders from communities in Brazil, Benin, Ghana and India; in each location, the team learns from local leaders what priorities they set for increasing the use of artificial intelligence to support informed decision making while working toward a just society.

Bio: Professor Danielle Wood serves as an Assistant Professor in Media Arts & Sciences and holds a joint appointment in the Department of Aeronautics & Astronautics at the Massachusetts Institute of Technology. Within the MIT Media Lab, Prof. Wood leads the Space Enabled Research Group. Prof. Wood is a scholar of societal development with a background that includes satellite design, earth science applications, systems engineering, and technology policy. In her research, Prof. Wood applies these skills to design innovative systems that harness space technology to address development challenges around the world and contribute to the long-term sustainability of outer space. Prior to serving as faculty at MIT, Professor Wood held positions at NASA Headquarters, NASA Goddard Space Flight Center, Aerospace Corporation, Johns Hopkins University, and the United Nations Office of Outer Space Affairs. Prof. Wood studied at the Massachusetts Institute of Technology, where she earned a PhD in engineering systems, SM in aeronautics and astronautics, SM in technology policy, and SB in aerospace engineering.

Organization

Organizing Committee

  • Arpita Biswas (Indian Institute of Science)
  • Eric Horvitz (Microsoft Research)
  • Andrew Perrault (Harvard University)
  • Sekou Remy (IBM Research Africa)
  • Sofia Segkouli (Information Technologies Institute, Thessaloniki, Greece)
  • Andreas Theodorou* (Umeå University)
  • Bryan Wilder* (Harvard University)

*primary contacts: bryan.wilder0@gmail.com, andreas.theodorou@umu.se

Program

All times are in Eastern Daylight Time (UTC−04:00).

Monday, July 20

9:00-9:15: Opening remarks

9:15-10:00: Contributed talks

Arjun Verma and Vikram Sarbajna. A deep learning based approach for monitoring sustainable farming practices at a parcel level.

Harold Kiossou, Yannik Schenk, Frédéric Docquier, Ratheil Houndji, Siegfried Nijssen and Pierre Schaus. Using an interpretable Machine Learning approach to study the drivers of International Migration.

Spotlight presentations (listed below)

10:00-11:00: Invited talk: Danielle Wood and Neil Gaikwad

11:00-12:00: Contributed talks

Christian Kammler, Annet Onnes, Loïs Vanhée, Harko Verhagen, Bart de Bruin, Paul Davidsson, Frank Dignum, Virginia Dignum, Amineh Ghorbani, Mijke van den Hurk, Maarten Jensen, Kurt Kreulen, Fabian Lorig, Luis Gustavo Ludescher, Alexander Melchior, René Mellema, Cezara Pastrav and Tomas Sjöström. Social Simulations for Intelligently Beating COVID-19.

Jakob Jonnerby, Philip Lazos, Edwin Lock, Francisco Marmolejo-Cossío, Christopher Bronk Ramsey and Divya Sridhar. Test and Contain: A Resource-Optimal Testing Strategy for COVID-19.

Spotlight presentations (listed below)

12:00-1:30: Poster session and break

1:30-2:45: Contributed talks

Anusua Trivedi, Kate Keator, Avirishu Verma, Rahul Dodhia, Ria Sankar and Juan Lavista Ferres. ECO: Using AI for Everyday Armed Conflict Analysis.

Tine Kolenik and Matjaž Gams. Progressing Social Good by Reducing Mental Health Care Inequality with Persuasive Technology.

Chao Yan, Haifeng Xu, Yevgeniy Vorobeychik, Bo Li, Daniel Fabbri and Bradley Malin. To Warn or Not to Warn: Online Signaling in Audit Games.

Brian Brubach, Aravind Srinivasan and Shawn Zhao. The Relationship between Gerrymandering Classification and Voter Incentives.

Taoan Huang and Bistra Dilkina. Enhancing Seismic Resilience of Water Pipe Networks.

2:45-3:00: Break

3:00-4:00: Invited talk: Ziad Obermeyer

4:00-4:30: Contributed talks

Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang and Milind Tambe. Dual-Mandate Patrols: Bandit-Based Learning in Green Security Domains.

Wanyi Li, Nicole Immorlica and Brendan Lucier. Contract Design for Afforestation Programs.

Tuesday, July 21

8:50-9:00: Opening remarks

9:00-10:00 Invited talk: Joanna Bryson

10:00-11:00: Contributed talks

Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K Patro, Manish Raghavan, Ana-Andreea Stoica and Stratis Tsirtsis. Fairness and Discrimination in Mechanism Design and Machine Learning.

Susan Leavy, Barry O’Sullivan and Eugenia Siapera. Data, Power and Bias in Artificial Intelligence.

Spotlight presentations (listed below)

11:00-12:00 Invited talk: ‪Vukosi Marivate‬

12:00-12:45: Break

12:45-2:00: Contributed talks

Siddharth Nishtala, Harshavardhan Kamarthi, Divy Thakkar, Dhyanesh Narayanan, Anirudh Grama, Aparna Hegde, Ramesh Padmanabhan, Neha Madhiwalla, Suresh Chaudhary, Balaraman Ravindran and Milind Tambe. Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement.

Kim de Bie, Nishant Kishore, Anthony Rentsch, Pablo Rosado and Andrea Sipka. Using AI to help healthcare professionals stay up-to-date with medical research.

Golnoosh Farnadi, Behrouz Babaki and Margarida Carvalho. Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration.

Caroline Johnston, Simon Blessenohl and Phebe Vayanos. Preference Elicitation and Aggregation to Aid with Patient Triage during the COVID-19 Pandemic.

Spotlight presentations (listed below)

2:00-3:00: Invited talk: Oisin Mac Aodha

3:00-3:15: Break

3:15-4:15 Contributed talks

Xingyu Chen and Zijie Liu. The Fairness of Leximin in Allocation of Indivisible Chores.

Ashiqur Khudabukhsh, Shriphani Palakodety and Jaime Carbonell. On NLP Methods Robust to Noisy Indian Social Media Data.

Spotlight presentations (listed below)

4:15-5:15 Poster session

Spotlight presentation schedule: Monday, July 20

9:15 session

Yassir Alharbi, Daniel Arribas-Bel and Frans Coenen. Sustainable Development Goal Relational Modelling: Introducing the SDG-RMF Methodology.

Ilkin Bayramli, Elizabeth Bondi and Milind Tambe. In the Shadow of Disaster: Finding Shadows to Improve Damage Detection.

Satyam Mohla, Sidharth Mohla and Anupam Guha. Green is the new Black: Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest.

Sai Venkata Ratna Rithwik Kukunuri, Nipun Batra, Ayush Pandey, Raktim Malakar, Rajat Kumar, Odysseas Krystalakos, Mingjun Zhong, Paulo Meira and Oliver Parson. NILMTK-Contrib: Towards reproducible state-of-the-art energy disaggregation.

11:00 session

Diana Diaz, Celia Cintas, William Ogallo and Aisha Walcott-Bryant. Towards Automatic Generation of Context-Based Abstractive Discharge Summaries for Supporting Transition of Care.

Alexandra Luccioni, Joseph Bullock, Katherine Hoffmann Pham, Cynthia Sin Nga Lam and Miguel Luengo-Oroz. Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19.

Raushan Raj, Anand Seetharam and Arti Ramesh. Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases.

Swaroop Gowdra, Anand Seetharam and Arti Ramesh. Understanding the Socio-Economic Disruption in the United States during COVID-19’s Early Days.

Athina Georgara, Carles Sierra and Juan-Antonio Rodríguez-Aguilar. Edu2Com: an anytime algorithm to form student teams in companies.

Zhigang Zhu, Jie Gong, Cecilia Feeley, Huy Vo, Hao Tang, Arber Ruci, Bill Seiple and Zheng Yi Wu. SAT-Hub: Smart and Accessible Transportation Hub for Assistive Navigation and Facility Management.

Anya Zhang and Andrew Perrault. Influence Maximization and Equilibrium Strategies in Election Network Games.

Subhro Das, Sebastian Steffen, Prabhat Reddy, Erik Brynjolfsson and Martin Fleming. Forecasting Task-Shares and Characterizing Occupational Change across Industry Sectors.

Melanie Laffin. Ethically Sourced Modeling: A Framework for Mitigating Bias in AI Projects within the US Government.

Caroline Trier and Lu Sevier. Designing a Partnership Framework in AI for Social Good.

Spotlight presentation schedule: Tuesday, July 21

10:00 session

Nawal Benabbou, Mithun Chakraborty, Ayumi Igarashi and Yair Zick. Finding Fair and Efficient Allocations When Valuations Don’t Add Up.

Robin Burke, Amy Voida, Nicholas Mattei and Nasim Sonboli. Algorithmic Fairness, Institutional Logics, and Social Choice.

Sean R. Sinclair, Gauri Jain, Siddhartha Banerjee and Christina Lee Yu. Sequential Fair Allocation of Limited Resources under Stochastic Demands.

Yan Zhou and Murat Kantarcioglu. On Transparency of Machine Learning Models: A Position Paper.

Deepak P. Whither Fair Clustering?

12:45 session

Guillaume Derval, Vincent François-Lavet and Pierre Schaus. Nowcasting COVID-19 hospitalizations using Google Trends and LSTM.

Bryan Wilder, Marie Charpingon, Jackson Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe and Maimuna Majumder. Inferring between-population differences in COVID-19 dynamics.

Swati Padhee, Tanay Kumar Saha, Joel Tetreault and Alejandro Jaimes. Clustering of Social Media Messages for Humanitarian Aid Response during Crisis.

Jawad Haqbeen, Takayuki Ito, Sofia Sahab, Rafik Hadfi, Shun Okuhara, Nasim Saba, Murtaza Hofiani and Umar Baregzai. A Contribution to COVID-19 Prevention through Crowd Collaboration using Con-versational AI & Social Platforms.

Ezinne Nwankwo, Chinasa Okolo and Cynthia Habonimana. Topic Modeling Approaches for Understanding COVID-19 MisinformationSpread in Sub-Saharan Africa.

3:15 session

Harshit Mishra. Reducing Word Embedding Bias Using Learned Latent Structure.

Lance Eliot. Position Paper: The Neglected Dualism Of Artificial Moral Agency And Artificial Legal Reasoning In AI For Social Good.

Tom Ron, Omer Ben-Porat and Uri Shalit. Corporate Social Responsibility via Multi-Armed Bandits.

Alaisha Sharma, Jackson Killian and Andrew Perrault. Optimization of the Low-Carbon Energy Transition Under Static and Adaptive Carbon Taxes via Markov Decision Processes.

Lily Xu, Andrew Perrault, Andrew Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba and Milind Tambe. Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers.

Jennifer Hobbs, Robert Paull, Bernard Markowicz and Greg Rose. Flowering density estimation from aerial imagery for automated pineapple flower counting.

Bob Bell and Rajesh Veeeraraghavan. Locating Informal Urban Settlements.

Sagar Vaze, Conrad James Foley, Mohamed Seddiq, Alexey Unagaev and Natalia Efremova. Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks.

Lynn Miller, Christoph Rüdiger and Geoffrey Webb. Using AI and Satellite Earth Observation to Monitor UN Sustainable Development Indicators.

Travel Awards

This iteration of the workshop is entirely virtual and will not have a travel awards program.