AI4SG-23

The field of Artificial Intelligence stands at an inflection point, and there could be many different directions in which the future of AI research could unfold. Accordingly, there is a growing interest to ensure that current and future AI research is used in a responsible manner for the benefit of humanity (i.e., for social good). To achieve this goal, a wide range of perspectives and contributions are needed, spanning the full spectrum from fundamental research to sustained deployments in the real-world.

This workshop will explore how AI research can contribute to solving challenging problems faced by current-day societies. For example, what role can AI research play in promoting health, sustainable development and infrastructure security? How can AI initiatives be used to achieve consensus among a set of negotiating self-interested entities (e.g., finding resolutions to trade talks between countries)? To address such questions, this workshop will bring together researchers and practitioners across different strands of AI research and a wide range of important real-world 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 be a very nice complement to the AAAI Special Track on AI for Social Impact as it will provide a forum where researchers interested in this area can connect in a more direct way.

This workshop complements the objectives of the main conference by providing a forum for AI algorithm designers, such as those working in the areas of agent-based modelling, machine learning, spatio-temporal models, deep learning, explainable AI, fairness, social choice, non-cooperative and cooperative game theory, convex optimization, and planning under uncertainty on innovative and impactful real-world applications. Specifically, this workshop serves two purposes. First, the workshop will provide an opportunity to showcase real-world deployments of AI research. More often than not, unexpected practical challenges emerge when solutions developed in the lab are deployed in the real world, which makes it challenging to utilize complex and well thought out computational/modeling advances. Learning about the challenges faced in these deployments during the workshop will help us understand lessons of moving from the lab to the real world. Second, the workshop will provide opportunities to showcase AI systems which dynamically adapt to changing environments, are robust to errors in execution and planning, and handle uncertainties of different kinds that are common in the real world. Addressing these challenges requires collaboration from different communities including machine learning, game theory, operations research, social science, and psychology. This workshop is structured to encourage a lively exchange of ideas between members from these communities. We encourage submissions to the workshop from: (i) computer scientists who have used (or are currently using) their AI research to solve important real-world problems for society’s benefit in a measurable manner; (ii) interdisciplinary researchers combining AI research with various disciplines (e.g., social science, ecology, climate, health, psychology and criminology); and (iii) engineers and scientists from organizations who aim for social good, and look to build real multi-agent systems.

Topics

• AISI: Agriculture/Food
• AISI: Assistive Technology for Well-Being
• AISI: Biodiversity or Habitat
• AISI: Computational Social Science
• AISI: Climate
• AISI: Education
• AISI: Economic/Financial
• AISI: Energy
• AISI: Environmental Sustainability
• AISI: Health and Well-Being
• AISI: Humanities
• AISI: Low and Middle-Income Countries
• AISI: Mobility/Transportation
• AISI: Natural Sciences
• AISI: Web or Social Networks
• AISI: Philosophical and Ethical Issues
• AISI: Security and Privacy
• AISI: Social Development
• AISI: Social Welfare, Justice, Fairness and Equality
• AISI: Urban Planning and Resilience
• AISI: Underserved Communities
• AISI: Socially Responsible AI: Fairness, Accountability, and Transparency
• AISI: Other Social Impact

Finally, the workshop will welcome papers that describe the release of benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples.

Format

The workshop will be a one-day meeting. It will include a number of (possibly parallel) technical sessions, a poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of AI for Social Good and learning and will conclude with a panel discussion.

Attendance

• November 20, 2022 AOE November 30, 2022 AOE – Submission Deadline
• December 14, 2022 – Acceptance Notifications to Authors
• January 14, 2023 – Camera Ready Versions Due Date
• February 14, 2023, 8 AM - 6 PM EST – Workshop Date & Time

Submission URL: Easychair Link

Submission Types

• Technical Papers: Full-length research papers of up to 7 pages (excluding references and appendices) detailing high quality work in progress or work that could potentially be published at a major conference in AAAI format.
• Short Papers: Position or short papers of up to 4 pages (excluding references and appendices) in AAAI format that describe initial work or the release of privacy-preserving benchmarks and datasets on the topics of interest.

Archival Policy

There will be no official proceedings of the workshop. Therefore, papers getting accepted and presented at this workshop does not preclude authors from submitting their work in other conferences or journals.

However, the default assumption is that all camera ready versions of the papers accepted (and presented) at this workshop will be uploaded to the workshop website after January 14th, 2023 (which is the due date for submitting camera-ready papers). If any author would not like to have their camera-ready versions uploaded to the this workshop website (because of some conflict of interest), please reach out to the workshop chairs and we would be happy to accommodate such requests.

Additional Details

All papers must be submitted in PDF format, using the AAAI-23 author kit. Submissions should include the name(s), affiliations, and email addresses of all authors.
Submissions will be refereed on the basis of technical quality, novelty, significance, and clarity. Each submission will be thoroughly reviewed by at least two program committee members.
Submissions of papers rejected from the AAAI 2023 technical program are welcomed.

For questions about the submission process, contact the workshop chairs.

• 8:30 AM - 9:20 AM    Poster Paper Session - Morning Session
• 9:20 AM - 9:30 AM    Welcome Notes by Workshop Organizers
• 9:30 AM - 10 AM    Keynote 1: Prof. Vipin Kumar
• 10:10 AM - 11:20 AM    Oral Paper Presentation Session (Healthcare)
• 11:30 AM - 12:00 PM    Keynote 2: Dr. Rahul Dodhia
• 12:00 PM - 12:30 PM    Panel Discussion 1 - Featuring Prof. Vipin Kumar and Dr. Rahul Dodhia
• 12:30 - 2 PM    Lunch Break
• 2:00 PM - 2:30 PM    Keynote 3: Dr. Philip Nelson
• 2:30 - 3:40 PM    Oral Paper Presentation Session (Computational Sustainability)
• 3:40 PM - 4:10 PM    Keynote 4: Dr. Amarjot Singh
• 4:10 PM - 4:40 PM    Panel Discussion 2 - Featuring Dr. Philip Nelson and Dr. Amarjot Singh
• 4:40 PM - 5:10 PM    Coffee Break
• 5:10 PM - 6 PM    Poster Paper Session - Evening Session

• 10:10 AM - 10:20 AM: Paritosh Verma, Shresth Verma, Aditya Mate, Aparna Taneja and Milind Tambe. Decision-Focused Evaluation: Analyzing Performance of Deployed Restless Multi-Arm Bandits. PDF
• 10:20 AM - 10:30 AM: Wenbo Zhang, Hangzhi Guo, Prerna Ranganathan, Jay Patel, Sathyanath Rajasekharan, Nidhi Danayak, Manan Gupta and Amulya Yadav. TRIM-AI: Harnessing Language Models for Providing Timely Maternal and Neonatal Care in Low-Resource Countries. PDF
• 10:30 AM - 10:40 AM: Joshua Chang, Ted Chang, Carson Chow, Rohit Mahajan, Sonya Mahajan, Joe Maisog, Shashaank Vattikuti and Hongjing Xia. Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death. PDF
• 10:40 AM - 10:50 AM: Emma Rocheteau, Ioana Bica, Pietro Liò and Ari Ercole. Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients. PDF
• 10:50 AM - 11:00 AM: Deepesh Kumar Lall, Garima Shakya and Swaprava Nath. Social Distancing via Social Scheduling. PDF
• 11:00 AM - 11:10 AM: Shresth Verma, Gargi Singh, Aditya Mate, Neha Madhiwalla, Aparna Hegde, Divy Thakkar, Manish Jain, Milind Tambe and Aparna Taneja. SAHELI for Mobile Health Programs in Maternal and Child Care: Further Analysis. PDF
• 11:10 AM - 11:20 AM: Jui Chien Lin, Farhad Mohsin, Sahith Bhamidipati and Lirong Xia. Generating Election Data Using Deep Generative Models. PDF

• 2:30 PM - 2:40 PM: Aaron Ferber, Emily Griffin, Bistra Dilkina, Burcu Keskin and Meredith Gore. Predicting Wildlife Trafficking Routes with Differentiable Shortest Paths. PDF
• 2:40 PM - 2:50 PM: Aniruddha Adiga, Surbhi Singh, Ethan Choo, Johnny Yang, Anjana Devkota, Bharat Babu Shrestha, Seerjana Maharjan, Sandeep Dhakal, Pramod Kumar Jha, Krishna Prasad Poudel, Rangaswamy Muniappan, Srinivasan Venkatramanan, Madhav Marathe and Abhijin Adiga. A Robust Deep Learning Framework Reveals the Spread of Multiple Invasive Plants in a Biodiversity Hotspot using Satellite Imagery. PDF
• 2:50 PM - 3:00 PM: Malvern Madondo, Kelsey DiPietro, Muneeza Azmat, Raya Horesh, Michael Jacobs, Arun Bawa, Raghavan Srinivasan and Fearghal O'Donncha. A SWAT-based Reinforcement Learning Framework for Crop Management. PDF
• 3:00 PM - 3:10 PM: Danu Kim, Jeongkyung Won, Eunji Lee, Kyung Ryul Park, Jihee Kim, Sangyoon Park, Hyunjoo Yang, Sungwon Park, Donghyun Ahn and Meeyoung Cha. Disaster assessment using computer vision and satellite imagery: Applications in water-related building damage detection. PDF
• 3:10 PM - 3:20 PM: Athiya Deviyani, Haris Widjaja, Mehak Malik and Daniel Hoskins. Men Have Feelings Too: Debiasing Sentiment Analyzers using Sequence Generative Adversarial Networks. PDF
• 3:20 PM - 3:30 PM: Liam Hebert, Hong Yi Chen, Robin Cohen and Lukasz Golab. Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content. PDF
• 3:30 PM - 3:40 PM: Garima Shakya and Makoto Yokoo. Balancing Fairness and Efficiency in 3D Repeated Matching in Ridesharing. PDF

• Ziwei Gu, Hongwen Song and Gauri Jain. BiomeAzuero2022: A Fine-Grained Dataset and Baselines For Tree Species Classification with Ground Images. PDF
• Matthew Nazari. NoLoR: An ASR-Based Framework for Expedited Endangered Language Documentation with Neo-Aramaic as a Case Study. PDF
• Noah Zweben, Elena Horton and Neeraj Chandra. Project HOPE: Homelessness Outreach Planning Effort. PDF
• Ching-Hao Chiu, Hao-Wei Chung, Yu-Jen Chen, Yiyu Shi and Tsung-Yi Ho. Fair Multi-Exit Framework for Facial Attribute Classification. PDF
• Kweku Kwegyir-Aggrey, Jessica Dai, A. Feder Cooper, John P. Dickerson and Keegan Hines. A Tale of Two Measures: Fair Classification at Any Decision Threshold. PDF
• Kumari Davis. Parsing the Landscape of AI Based Mental Health Applications: Thoughts and Considerations on Feedback from End Users. PDF
• Abhinav Kumar Thakur, Filip Ilievski, Hông- n Sandlin, Alain Mermoud, Zhivar Sourati, Luca Luceri and Riccardo Tommasini. Multimodal and Explainable Internet Meme Classification. PDF
• Yejin Bang, Nayeon Lee, Tiezheng Yu, Leila Khalatbari, Yan Xu, Samuel Cahyawijaya, Dan Su, Bryan Wilie, Romain Barraud, Elham J. Barezi, Andrea Madotto, Hayden Kee and Pascale Fung. Towards Answering Open-ended Ethical Quandary Questions. PDF
• Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Arun Ravindran and Hamed Tabkhi. Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety. PDF
• Akash Saravanan, Dhruv Mullick, Habib Rahman and Nidhi Hegde. FineDeb: A Debiasing Framework for Language Models. PDF
• Marc Grimson, Qinru Shi, Yiwei Bai, Alexander Flecker and Carla Gomes. Scaling Up the Pareto Frontier for Tree Structures with Affine Transformations. PDF
• Joshua Chang, Carson Chow and Julia Porcino. Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery. PDF
• Manjish Pal, Subham Pokhriyal and Niloy Ganguly. Ensuring Demographic Parity and Equalized Odds in Batch Classification. PDF (removed at author request)
• Isha Puri, Neil Sehgal and Usha Bhalla. Reconsidering the Algorithmic Fairness of Race Adjustment in Pulmonary Function Equations.PDF
• Anuradha Singh, Jyoti Yadav, Sarahana Shrestha and Aparna Varde. Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index. PDF
• Toan Tran and Mina Sartipi. Revisiting Pixel-based Traffic Signal Controls using Reinforcement Learning with World Models. PDF

• Milan Jain, Narmadha Meenu Mohankumar, Heng Wan, Sumitrra Ganguly, Kyle D Wilson and David M Anderson. Training Machine Learning Models to Characterize Temporal Evolution of Disadvantaged Communities. PDF
• April Chen and Prajna Soni. FARE: Fair Allocation RE-weighting. PDF (removed at author request)
• Nalin Semwal, Manan Suri, Divya Chaudhary, Ian Gorton and Bijendra Kumar. Multimodal Analysis and Modality Fusion for Detection of Depression from Twitter Data. PDF
• Supriti Vijay, Aman Priyanshu and Ashalatha Nayak. F-BRIM: A Semi-Supervised Approach for Bias Mitigation with Activation-Weighted Neuron Regularization. PDF (removed at author request)
• Lucia Gordon and Samuel Collier. Detecting Rhino Middens with Computer Vision and Active Learning: Preliminary Results. PDF
• Zhiyao Luo, Peter Watkinson and Tingting Zhu. NurSpecialist: Duel-Agent Reinforcement Learning for Dynamic Hospitalised Intervention Regimes using Electronic Health Records. PDF
• Hari Prasanna Das and Costas J. Spanos. Improved Dequantization and Normalization Methods for Tabular Data Pre-Processing in Smart Buildings. PDF
• Bo Lin, Timothy Chan and Shoshanna Saxe. AutoLTS: A Computer Vision Approach to Assessing Cycling Stress. PDF
• Hongjin Lin, Matthew Nazari and Derek Zheng. PCTreeS — 3D Point Cloud Tree Species Classification Using Airborne LiDAR Images. PDF
• Jackson Killian, Arshika Lalan, Aditya Mate, Manish Jain, Aparna Taneja and Milind Tambe. Adherence Bandits. PDF
• Saeid Shamsaliei, Odd Erik Gundersen, Knut Alfredsen and Jo Halvard Halleraker. Towards Historical Analysis of Riverscape Development Utilizing Semantic Segmentation. PDF
• Amy Jin and Maximillian Guo. Using Satellite Imagery to Predict Multidimensional Poverty in Nigeria. PDF

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