AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefore crucial to understand how they will impact climate action. On the one hand, AI can support applications in climate change mitigation (reducing or preventing greenhouse gas emissions), adaptation (preparing for the effects of a changing climate), and climate science. These applications have implications in areas ranging as widely as energy, agriculture, and finance. At the same time, AI is used in many ways that hinder climate action (e.g., by accelerating the use of greenhouse gas-emitting fossil fuels). In addition, AI technologies have a carbon and energy footprint themselves. This symposium brought together participants from across academia, industry, government, and civil society to explore these intersections of AI with climate change, as well as how each of these sectors can contribute to solutions.
About the AAAI Fall Symposium Series
This symposium was part of the AAAI Fall Symposium Series, which aims to provide a forum to present ongoing work, hold focused discussions, build new communities for emerging disciplines, and build ties between existing disciplines. The Symposium Series is run via the Association for the Advancement of Artificial Intelligence (AAAI), the premier international scientific society devoted to promoting research in, and the responsible use of, artificial intelligence. Symposia are open to the public; it is not necessary to submit to the symposium in order to attend, though submissions are strongly encouraged.
About the Symposium
- Dates: Nov 17-19, 2022
- Location: Westin Arlington Gateway, Arlington, Virginia, USA
Paper submission deadline: Jul 29, 23:59 Anywhere on Earth (AOE)
- Registration site: https://aaai.org/Symposia/Fall/fss22.php
- Contact: email@example.com
The 2022 Fall Symposium Series was a primarily in-person event. The guidelines provided by AAAI were as follows:
- AAAI requires all attendees be vaccinated, unless they have a documented medical exception. An email will be sent a few weeks prior to the conference with instructions on how to upload your vaccination information.
- Masks are suggested for all in-door activities. Please bring your own mask if you plan on wearing one.
- For anyone who tests positive 1 week or sooner before the conference, AAAI will offer a full refund.
The symposium ran from November 17-19 in Arlington, VA, USA, and included keynote talks, panels, presentations of contributed work, poster sessions, and discussion sessions.
Jump to: Thursday (17th), Friday (18th), Saturday (19th)
Thursday, November 17
|Welcome and Opening Remarks|
|Overview of Conference Themes|
Burcu Akinci: Digital Twins and Context-aware AI for Energy Efficient Buildings (Keynote)
Details: (click to expand)Speaker Bio: Dr. Burcu Akinci is the Paul Christiano Professor of Civil & Environmental Engineering at Carnegie Mellon University and a member of the National Academies of Construction. Her research interests include modeling and reasoning about information rich histories of buildings and infrastructure systems, to streamline construction and infrastructure operations. She specifically focuses on investigating utilization and integration of building information models with data capture technologies, such as 3D imaging and embedded sensors, to create digital twins of construction projects and infrastructure operations, and develop approaches to support proactive and predictive operations and management. Dr. Akinci has two patents, and one provisional patent and over 70 refereed journal publications and 100 conference publications. She was the PI of more than $6M grants and co-PI of more than $10M grants, supported by federal and state agencies and industry. She has given over 100 invited presentations and co-edited books on CAD/GIS Integration and on Embedded Commissioning. She earned a bachelor’s degree in civil engineering from the Middle East Technical University (Ankara, Turkey), MBA from Bilkent University (Ankara, Turkey), and Master’s and PhD degrees in Civil and Environmental Engineering with a specialization in Construction Engineering and Management from Stanford University.
|Adiba Proma: NADBenchmarks - a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters (Contributed Paper)|
|Laure Berti-Equille: Discovering Transition Pathways Towards Coviability with Machine Learning (Contributed Paper)|
|Kim Bente: Probabilistic Machine Learning in Polar Earth and Climate Science: A Review of Applications and Opportunities (Contributed Paper)|
Troy Harvey: Architecting a Path Toward Generalized Autonomy: Addressing the Biggest Opportunity in Decarbonization (Keynote)
Details: (click to expand)Speaker Bio: Troy Harvey is CEO and co-founder of PassiveLogic, the creator of the first platform for generalized autonomy. As architect of the Quantum Digital Twin standard and Deep Physics AI engine, his empathic, systems-oriented approach to technology development is transforming the way we control systems and equipment. Optimizing buildings, cities, and other controlled systems is the clearest opportunity Troy sees to contribute to the world’s most pressing climate challenges.
|Jose Manuel Carbo: Machine Learning Methods in Climate Finance: A Systematic Review (Contributed Paper)|
|Alix Auzepy: The Impact of TCFD Reporting - A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures (Contributed Paper)|
|Tristan Ballard: Contrastive Learning for Climate Model Bias Correction and Super-Resolution (Contributed Paper)|
|Paula Harder: Generating physically-consistent high-resolution climate data with hard-constrained neural networks (Contributed Paper)|
|Matthew Cooper: Predicting Wildfire Risk Under Novel 21st-Century Climate Conditions (Contributed Paper)|
|Rendani Mbuvha: Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic (Contributed Paper)|
|Sebastian Hickman & Paul Griffiths, "Predicting Daily Ozone Air Pollution With Transformers"|
|Pedro Roberto Barbosa Rocha, "Data-Driven Reduced-Order Model for Atmospheric CO2 Dispersion"|
|Arjun Ashok, "Self-Supervised Representations of Geo-located Weather Time Series - An Evaluation and Analysis"|
|Thai-Nam Hoang, "Wildfire Forecasting with Satellite Images and Deep Generative Model"|
Panel: Where is the funding, and is it sufficient to meet the 2030 and 2050 goals?
Details: (click to expand)Panelists: Dr. Fahmida Chowdhury (NSF), Dr. David Tew (ARPA-E), Jon Greene (ITA International), Andrés Alonso Robisco (Banco de España)
Moderator: Dr. Jim Spohrer (ISSIP, formerly IBM)
Friday, November 18
|Day 2 Welcome|
Pamela K. Isom: Climate Innovations and the Role of AI and Cyber" (Keynote)
Details: (click to expand)Description: Mrs. Pamela K. Isom will share her personal perspectives and knowledge on the climate crisis and the roles AI and Cybersecurity must play towards economic prosperity and global security. During her talk, Isom will highlight examples of climate discoveries and explore AI patterns for sustainment. Some examples:
|Aryan Jain: Employing Deep Learning to Quantify Power Plant Greenhouse Gas Emissions via Remote Sensing Data (Contributed Paper)|
|Yeji Choi: Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network (Contributed Paper)|
|Junjie Xu: From Ideas to Deployment - A Joint Industry-University Research Effort on Tackling Carbon Storage Challenges with AI (Contributed Paper)|
|Aron Brenner: Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints (Contributed Paper)|
|Huseyin Tuna Erdinc: De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images (Contributed Paper)|
|Aditya Grover: Rethinking Machine Learning for Climate Science: A Dataset Perspective (Contributed Paper)|
|Tianyu Zhang & Stephan Zheng: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N (Contributed Paper)|
Ranveer Chandra: FarmVibes.AI: Democratizing Digital Tools for Sustainable Agriculture" (Keynote)
Details: (click to expand)Speaker Bio: Dr. Ranveer Chandra is the Managing Director for Research for Industry, and the CTO of Agri-Food at Microsoft. He also leads the Networking Research Group at Microsoft Research, Redmond. Previously, Ranveer was the Chief Scientist of Microsoft Azure Global. His research has shipped as part of multiple Microsoft products, including VirtualWiFi in Windows 7 onwards, low power Wi-Fi in Windows 8, Energy Profiler in Visual Studio, Software Defined Batteries in Windows 10, and the Wireless Controller Protocol in XBOX One. His research also led to a new product, called Azure FarmBeats. Ranveer has published more than 100 papers, and holds over 150 patents granted by the USPTO. His research has been cited by the popular press, such as the Economist, MIT Technology Review, BBC, Scientific American, New York Times, WSJ, among others. He is a Fellow of the IEEE, and has won several awards, including best paper awards at ACM CoNext 2008, ACM SIGCOMM 2009, IEEE RTSS 2014, USENIX ATC 2015, Runtime Verification 2016 (RV’16), ACM COMPASS 2019, and ACM MobiCom 2019, the Microsoft Research Graduate Fellowship, the Microsoft Gold Star Award, the MIT Technology Review’s Top Innovators Under 35, TR35 (2010) and Fellow in Communications, World Technology Network (2012). He was recently recognized by the Newsweek magazine as America’s 50 most Disruptive Innovators (2021). Ranveer has an undergraduate degree from IIT Kharagpur, India and a PhD from Cornell University.
|Nicolas Webersinke: ClimateBert: A Pretrained Language Model for Climate-Related Text (Contributed Paper)|
|Md Saiful Islam & Adiba Proma: KnowUREnvironment: An Automated Knowledge Graph for Climate Change and Environmental Issues (Contributed Paper)|
|Tarun Narayanan & Ajay Krishnan, "Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning"|
|John Aitken & Denali Rao, "AI-Based Text Analysis for Evaluating Food Waste Policies"|
|Nitpreet Bamra, "Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms"|
|Xin Zhou: Using Natural Language Processing for Automating the Identification of Climate Action Interlinkages within the Sustainable Development Goals (Contributed Paper)|
Panel: What governance and actions are necessary to align AI with climate change goals, the UN Sustainable Development Goals, and associated ESG frameworks?
Details: (click to expand)Panelists: Dr. Lance Eliot (Stanford Fellow, Techbrium Incorporated), Serge Conesa (Immersion4), Dr. Bosen Liu (ITU, UNESCO), Allison Rogers (Aspen Institute, Second Nature), John C. Havens (IEEE Standards Association)
Moderator: Pierre-Adrien Hanania (Capgemini)
|Workshop Discussion & Readout Prep|
|Cross-Symposium Plenary Session|
Saturday, November 19
Group Breakout Discussion: The Way Forward on Aligning AI with Climate Action
Details: (click to expand)Join us for a group breakout discussion to brainstorm concrete steps and the way forward on aligning AI with climate action.
Moderator: Dr. Priya Donti (Climate Change AI)
|Group Breakout Discussion Ctd. & Sharing of Next Steps|
|(1) AI-Based Text Analysis for Evaluating Food Waste Policies||John Aitken (The MITRE Corporation), Denali Rao (The MITRE Corporation), Balca Alaybek (The MITRE Corporation), Amber Sprenger (The MITRE Corporation), Grace Mika (The MITRE Corporation), Rob Hartman (The MITRE Corporation) and Laura Leets (The MITRE Corporation)|
|(2) Data-Driven Reduced-Order Model for Atmospheric CO2 Dispersion||Pedro Roberto Barbosa Rocha (IBM Research), Marcos Sebastião de Paula Gomes (Pontifical Catholic University of Rio de Janeiro), João Lucas de Sousa Almeida (IBM Research), Allan Moreira Carvalho (IBM Research) and Alberto Costa Nogueira Junior (IBM Research)|
|(3) KnowUREnvironment: An Automated Knowledge Graph for Climate Change and Environmental Issues||Md Saiful Islam (University of Rochester), Adiba Proma (University of Rochester), Yilin Zhou (University of Rochester), Syeda Nahida Akter (Carnegie Mellon University), Caleb Wohn (University of Rochester) and Ehsan Hoque (University of Rochester)|
|(4) Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms||Nitpreet Bamra (University of Waterloo), Vikram Voleti (Mila, University of Montreal), Alexander Wong (University of Waterloo) and Jason Deglint (University of Waterloo)|
|(5) Generating physically-consistent high-resolution climate data with hard-constrained neural networks||Paula Harder (Fraunhofer Institute ITWM, Mila Quebec AI Institute), Qidong Yang (Mila Quebec AI Institute, New York University), Venkatesh Ramesh (Mila Quebec AI Institute, University of Montreal), Alex Hernandez-Garcia (Mila Quebec AI Institute, University of Montreal), Prasanna Sattigeri (IBM Research), Campbell D. Watson (IBM Research), Daniela Szwarcman (IBM Research) and David Rolnick (Mila Quebec AI Institute, McGill University).|
|(6) Discovering Transition Pathways Towards Coviability with Machine Learning||Laure Berti-Equille (IRD) and Rafael Raimundo (UFPB)|
|(7) Wildfire Forecasting with Satellite Images and Deep Generative Model||Thai-Nam Hoang (University of Wisconsin - Madison), Sang Truong (Stanford University) and Chris Schmidt (University of Wisconsin - Madison)|
|(8) From Ideas to Deployment - A Joint Industry-University Research Effort on Tackling Carbon Storage Challenges with AI||Junjie Xu (Tsinghua University), Jiesi Lei (Tsinghua University), Yang Li (Tsinghua University), Junfan Ren (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing, China), Jian Qiu (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Biao Luo (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Lei Xiao (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China) and Wenwen Zhou (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China)|
|(9) NADBenchmarks - a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters||Adiba Proma (University of Rochester), Md Saiful Islam (University of Rochester), Stela Ciko (University of Rochester), Raiyan Abdul Baten (University of Rochester) and Ehsan Hoque (University of Rochester)|
|(10) Contrastive Learning for Climate Model Bias Correction and Super-Resolution||Tristan Ballard (Sust Global) and Gopal Erinjippurath (Sust Global)|
|(11) Employing Deep Learning to Quantify Power Plant Greenhouse Gas Emissions via Remote Sensing Data||Aryan Jain (Amador Valley High School)|
|(12) ClimateBert: A Pretrained Language Model for Climate-Related Text||Nicolas Webersinke (FAU Erlangen-Nürnberg), Mathias Kraus (FAU Erlangen-Nürnberg), Julia Anna Bingler (ETH Zurich) and Markus Leippold (UZH Zurich)|
|(13) Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning||Tarun Narayanan (SpaceML), Ajay Krishnan (SpaceML), Anirudh Koul (Pinterest, SpaceML, FDL) and Siddha Ganju (NVIDIA, SpaceML, FDL)|
|(14) De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images||Huseyin Tuna Erdinc (Georgia Institute of Technology), Abhinav Prakash Gahlot (Georgia Institute of Technology), Ziyi Yin (Georgia Institute of Technology), Mathias Louboutin (Georgia Institute of Technology) and Felix J. Herrmann (Georgia Institute of Technology)|
|(15) Predicting Wildfire Risk Under Novel 21st-Century Climate Conditions||Matthew Cooper (Sust Global).|
|(16) Probabilistic Machine Learning in Polar Earth and Climate Science: A Review of Applications and Opportunities||Kim Bente (The University of Sydney), Judy Kay (The University of Sydney) and Roman Marchant (Commonwealth Scientific and Industrial Research Organisation (CSIRO))|
|(17) Rethinking Machine Learning for Climate Science: A Dataset Perspective||Aditya Grover (UCLA)|
|(18) Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints||Aron Brenner (MIT), Rahman Khorramfar (MIT), Dharik Mallapragada (MIT) and Saurabh Amin (MIT)|
|(19) Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N||Tianyu Zhang (Université de Montréal, MILA), Andrew Williams (Université de Montréal, MILA), Soham Phade (Salesforce Research), Sunil Srinivasa (Salesforce Research), Yang Zhang (MILA), Prateek Gupta (MILA, University of Oxford, The Alan Turing Institute), Yoshua Bengio (Université de Montréal, MILA, CIFAR) and Stephan Zheng (Salesforce Research)|
|(20) Self-Supervised Representations of Geo-located Weather Time Series - an Evaluation and Analysis||Arjun Ashok (IBM Research), Devyani Lambhate (IBM Research) and Jitendra Singh (IBM Research)|
|(21) Predicting Daily Ozone Air Pollution With Transformers||Sebastian Hickman (University of Cambridge), Paul Griffiths (University of Cambridge), Peer Nowack (University of East Anglia) and Alex Archibald (University of Cambridge)|
|(22) The Impact of TCFD Reporting - A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures||Alix Auzepy (Justus-Liebig-Universität Gießen), Elena Tönjes (Justus-Liebig-Universität Gießen) and Christoph Funk (Justus-Liebig-Universität Gießen)|
|(23) Using Natural Language Processing for Automating the Identification of Climate Action Interlinkages within the Sustainable Development Goals||Xin Zhou (Institute for Global Environmental Strategies (IGES)), Kshitij Jain (Google Inc.), Mustafa Moinuddin (Institute for Global Environmental Strategies (IGES)) and Patrick McSharry (Carnegie Mellon University Africa; Oxford Man Institute of Quantitative Finance, Oxford University)|
|(24) Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic||Rendani Mbuvha (Queen Mary University of London), Julien Yise Peniel Adounkpe (International Water Management Institute (IWMI)), Wilson Tsakane Mongwe (University of Johannesburg), Mandela Houngnibo (Agence Nationale de la Météorologie du Benin Meteo Benin), Nathaniel Newlands (Summerland Research and Development Centre, Agriculture and Agri-Food Canada) and Tshilidzi Marwala (University of Johannesburg)|
|(25) Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network||Minseok Seo (SI Analytics), Yeji Choi (SI Analytics), Hyungon Ryu (NVIDIA), Heesun Park (National Institute of Meteorological Science), Hyungkun Bae (SI Analytics), Hyesook Lee (National Institute of Meteorological Science) and Wanseok Seo (NVIDIA)|
|(26) Machine Learning Methods in Climate Finance: A Systematic Review||Andres Alonso-Robisco (Banco de España), Jose Manuel Carbo (Banco de España) and Jose Manuel Marques (Banco de España)|
Feras A. Batarseh (Virginia Tech)
Priya L. Donti (Climate Change AI, MIT) - Co-Chair
Ján Drgoňa (PNNL)
Kristen Fletcher (Naval Postgraduate School)
Pierre-Adrien Hanania (Capgemini)
Melissa Hatton (Capgemini Government Solutions) - Co-Chair
Srinivasan Keshav (University of Cambridge)
Bran Knowles (Lancaster University)
Raphaela Kotsch (University of Zurich)
Sean McGinnis (Virginia Tech)
Peetak Mitra (PARC)
Alex Philp (Mitre)
Jim Spohrer (ISSIP)
Frank Stein (Virginia Tech) - Co-Chair
Meghna Tare (UT Arlington)
Svitlana Volkov (PNNL)
Gege Wen (Stanford)
Call for Submissions
We invite submissions of position, review, and research papers in two formats: short papers (2-4 pages) and full papers (6-8 pages). Submissions are due on Jul 29 by 23:59 AOE (Anywhere on Earth).
Topics may include, but are not limited to:
- AI applications and methods for climate change mitigation, adaptation, and climate science (across all societal sectors, including agriculture, buildings, heavy industry, power and energy, transportation, and forestry), and supporting societal priorities such as energy security, climate security, and climate equity.
- The use of AI to analyze, synthesize, and evaluate pathways to achieve carbon neutrality (e.g., energy sector transition plans from fossil fuels to low-carbon technologies) and for applications in climate change mitigation-related policy more broadly.
- The use of AI to understand and/or alleviate the effect of climate change on economies, society, production, conflict, and international trade, and for applications in climate change adaptation-related policy more broadly.
- Considerations and frameworks for the development, deployment, and evaluation of AI-based climate solutions (e.g., standards; best practices; governance; ethical frameworks; mechanisms for stakeholder engagement; and additional approaches for fostering accountability, transparency, and trustworthiness).
- Mechanisms for developing, deploying, scaling, and evaluating AI-based climate solutions (e.g. funding, data ecosystems, regulatory frameworks, and public-private partnerships).
- Methodologies and frameworks for assessing the climate impacts of AI technologies in general (e.g., increased computational energy demand, the effects of applications, and broader systemic effects), including strategies for measurement and reporting.
- Governance and policies required to align the use of AI with societal climate change goals, the UN Sustainable Development Goals, and associated ESG frameworks.
All submissions must make clear their connection to these topics and/or the broader theme of the workshop. Extended versions of articles in submission at other venues are acceptable as long as they do not violate the dual-submission policy of the other venue.
All submissions must be made through the EasyChair submission website (select “The Role of AI in Responding to Climate Challenges” track), and should be formatted according to the AAAI style template. All submissions will undergo single-blind peer review (that is, submissions should not be anonymized). Authors will have the option to publish their work in an open access proceedings site.
Please also see the FAQ, and contact firstname.lastname@example.org with questions.
We invite submissions of position, review, and research papers (more details below).
We invite both short papers (2-4 pages) and full papers (6-8 pages). All figures, tables, and graphics must be contained within these page limits; however, references may extend onto additional pages. Supplementary appendices are allowed but will be read at the discretion of the reviewers.
Work that is in progress, published, and/or deployed.
Research Papers should describe projects relevant to the intersection of climate change and AI. These may include (but are not limited to) academic research; deployed results from startups, industry, public institutions, etc.; and climate-relevant datasets. In addition to describing the projects themselves, Research Papers should also include discussion of lessons learned, best practices, and areas whether further research and innovation is required.
Submissions applying AI to address a climate-relevant problem should provide experimental or theoretical validation of the method presented, as well as specifying what gap the method fills. Authors should clearly illustrate a pathway to climate impact, i.e., identify the way in which this work fits into broader efforts to address climate change. Algorithms need not be novel from an AI perspective, but should be applied in a climate-relevant setting. Details of methodology need not be revealed if they are proprietary, though transparency is highly encouraged.
Submissions presenting novel climate-relevant datasets are welcomed. Datasets should be designed to permit AI and machine learning research (e.g., formatted with clear benchmarks for evaluation). In this case, baseline experimental results on the dataset are preferred, but not required.
Overviews of relevant topics or fields.
Review Papers should provide overviews of topics or fields at the intersection of climate change and AI (e.g., pertaining to the topics listed in the call for submissions).
Submissions of this form should synthesize existing literature, and additionally provide insights on gaps, salient considerations, and future directions to be considered by symposium attendees and the broader community.
Opinions or critiques on relevant topics or directions.
Position Papers should provide opinions or critiques on topics or directions at the intersection of climate change and AI (e.g., pertaining to the topics listed in the call for submissions).
Submissions of this form should present important frameworks or considerations for work at the intersection of climate change and AI, and should be well-grounded in existing literature and/or practice.
Frequently Asked Questions
Q: I didn’t submit a paper to the symposium. Can I still attend?
A: Yes! Please register via the AAAI website.
Q: What is the COVID policy of the event?
A: We take COVID-19 very seriously, and will at minimum strictly adhere to all guidelines provided by AAAI. Please see more information above.
Q: I am interested in the topics of the symposium, but I cannot attend in person. Can I still participate?
A: Yes, registered attendees will be able to participate through Zoom. Because we are using hotel facilities, the quality of the virtual experience is out of our control. We recommend in-person participation to get the most benefit from this interactive symposium.
Q: How can I keep up to date on these issues?
A: We encourage you to sign up for the Climate Change AI newsletter to receive information about opportunities and events at the intersection of climate change and AI.
Q: I’m not an AI researcher or practitioner. Can I still submit?
A: Yes, absolutely! We welcome submissions from a diverse set of stakeholders.
Q: What if my submission is accepted but I can’t attend the symposium in person?
A: A co-author or colleague can present for you, or you may be able to present via Zoom.
Q: Do I need to use the AAAI style files for my submission?
A: Yes, they are required.
Q: It’s hard for me to fit my submission within the page limits. What should I do?
A: Feel free to include appendices with additional material (these should be part of the same file as the main submission). Do not, however, put essential material in an appendix, as it will be read at the discretion of the reviewers.
Q: Can I send submissions directly by email?
A: No, please use the EasyChair website to make submissions, and select the track on “The Role of AI in Responding to Climate Challenges” when creating your submission. If you are having trouble with the EasyChair website, please contact: email@example.com.
Q: Can I submit previously published work to this symposium?
A: Yes, though under limited circumstances. In particular, extended versions of articles in submission at other venues are acceptable as long as they do not violate the dual-submission policy of the other venue. Please contact firstname.lastname@example.org with any questions.
Q: Can I submit work to this symposium if I am also submitting to another 2022 AAAI Fall Symposium session?
A: Yes. We cannot, however, guarantee that your assigned presentation time will not conflict with the other symposium.
Q: How will my submission be published?
A: We plan to publish proceedings on arXiv after the symposium. Please note that inclusion in the proceedings is optional - we encourage you to submit to and present at the workshop, even if you do not want your work to appear in the proceedings.
Q: If my work appears in the proceedings, can I also publish this work elsewhere?
A: All submissions to this symposium are “non-archival” - that is, from our perspective, choosing to include your work in the proceedings does not preclude future publication in another venue. However, we recommend that you also check the policies of the venue to which you might plan to submit (e.g., whether they allow publication of work that was previously posted as a preprint).