Recent developments in the availability of big data and computational power are continuing to revolutionize several domains opening up new opportunities and challenges. In this symposium, we highlight two specific themes of humanitarian relief and healthcare where AI could be used for social good to achieve the United Nations (UN) sustainable development goals in those areas, which touch every aspect of human, social, and economic development. We expect the symposium to identify the critical needs and pathways for responsible AI solutions for achieving the sustainable goals, which demand holistic thinking on optimizing the trade-off between automation benefits and their potential side-effects.
AI technology can have an incredible impact on how we address humanitarian issues and achieve developmental goals around the world. Detecting and predicting how a crisis or conflict could develop, analyzing the impact of catastrophes in a cyber-physical society, and assisting in disaster response as well as resource allocation are of utmost importance, and the advances in AI can be utilized in many such tasks. These techniques can allow better preparation for emergencies, help save lives, limit economic losses, provide adequate disaster relief, and make communities stronger and more resilient. This symposium will focus on all aspects of humanitarian relief operations supported by the novel use of AI technologies from enabling missing persons to be located, leveraging crowdsourced data to provide early warning for rapid response to emergencies, increasing situational awareness, to logistics and supply chain management.
Similarly, healthcare is one of the foremost challenges of today’s world, highlighted by the recent COVID-19 pandemic where it has come to the forefront of the global discourse. In general, the healthcare data is characterized by missing data, lack of data standardization, data incompleteness, etc., which hinders the deployment of solutions that are relevant to real-world use cases. Moreover, AI/ML in healthcare is characterized by the last mile problem, where delivering a practical solution that is reliable, robust to errors (especially in “break glass in case of emergency” situations), and graceful degradation has proven hard to implement. These have broader implications in the context of fairness, explainability, and transparency. Therefore, implementation and deployment of AI/ML systems in production in healthcare bring up challenges which go far beyond model building and scoring. This symposium will also focus on a broad range of AI healthcare applications and challenges encountered including but not limited to: automation bias, prescriptive AI models, explainability, privacy and security, transparency, decision rights, and so on, especially in the context of deployment of AI in healthcare.
The proposed symposium builds upon our continued efforts in bringing the AI community members together for the above-mentioned themes and also, our last year’s successful AAAI Fall Series symposium on AI for Social Good. Further, our symposium builds upon the lessons from the 2015 AAAI Spring Symposium on Structured Data for Humanitarian Technologies organized by some of the authors of this proposal.
This symposium will bring together AI researchers, domain scientists, and policymakers to exchange problems and solutions, to identify synergies across different application domains, and to lead to future collaborative efforts.
This symposium solicits paper submissions from participants (2-6 pages) in either of the two disciplines described below:
Papers (2-6 pages) of the following flavors are sought:
All submissions will be peer-reviewed. Some will be selected for spotlight talks, and some for the poster session. We plan to create a CEUR workshop proceeding.
Submit papers here: https://easychair.org/conferences/?conf=fss20
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