2021 IJCAI Workshop on AI for Social Good

2020
Trivedi A, Keator K, Verma A, Dodhia R, Ferres JL. ECO: Using AI for Everyday Armed Conflict Analysis, in AI for Social Good Workshop. ; 2020.Abstract

Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Transformation of armed conflict data tends to be a manual, time consuming task that nonprofits with limited budgets struggle to keep up with. In this paper, we explore the use of a deep natural language processing (NLP) model to aid the transformation of armed conflict data for conflict analysis. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Thus our model contributes to the incorporation of technology in the peace building and conflict resolution sector.

Back to AI for Social Good event

ECO: Using AI for Everyday Armed Conflict Analysis
P D. Whither Fair Clustering?, in AI for Social Good Workshop. ; 2020.Abstract

Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the- art in fair clustering has been quite parochial in out- look. We posit that widening the normative prin- ciples to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance.

Back to AI for Social Good event

Whither Fair Clustering?
Huang T, Dilkina B. Enhancing Seismic Resilience of Water Pipe Networks, in AI for Social Good Workshop. ; 2020.Abstract

As disasters such as earthquakes and floods be- come more frequent and detrimental, it is increas- ingly important that water infrastructure resilience be strategically enhanced to support post-disaster functionality and recovery. In this paper, we focus on the problem of strategically building seismic- resilient pipe networks to ensure direct water sup- ply to critical customers and certain proximity to water sources for residential areas, which we for- malize as the Steiner network problem with cov- erage constraints. We present an efficient mixed- integer linear program encoding to solve the prob- lem. We also investigate the problem of planning partial network installments to maximize efficiency over time and propose an effective sequential plan- ning algorithm to solve it. We evaluate our algo- rithms on synthetic water networks and apply them to a case study on a water service zone in Los An- geles, which demonstrate the effectiveness of our methods for large-scale real-world applications.

Back to AI for Social Good event

Enhancing Seismic Resilience of Water Pipe Networks
Zhu Z, Gong J, Feeley C, Vo H, Tang H, Ruci A, Seiple B, Wu ZY. SAT-Hub: Smart and Accessible Transportation Hub for Assistive Navigation and Facility Management, in AI for Social Good Workshop. ; 2020.Abstract

The goal of the proposed project is to transform a large transportation hub into a smart and accessible hub (SA T-Hub), with minimal infrastructure change. The societal need is significant, especially impactful for people in great need, such as those who are blind and visually impaired (BVI) or with Autism Spectrum Disorder (ASD), as well as those unfamiliar with metropolitan areas. With our inter- disciplinary background in urban systems, sensing, AI and data analytics, accessibility, and paratransit and assistive services, our solution is a hu- man-centric system approach that integrates facility modeling, mobile navigation, and user interface designs. We leverage several transportation facili- ties in the heart of New York City and throughout the State of New Jersey as testbeds for ensuring the relevance of the research and a smooth transition to real world applications.

Back to AI for Social Good event

SAT-Hub: Smart and Accessible Transportation Hub for Assistive Navigation and Facility Management
Miller L, Rüdiger C, Webb GI. Using AI and Satellite Earth Observation to Monitor UN Sustainable Development Indicators, in AI for Social Good Workshop. ; 2020.Abstract

There is widespread acceptance that data from earth observation satellites, combined with artificial intelligence, have the potential to play an important role to enable the quantification of the United Nations Sustainable Development Indicators (SDIs). However, building workflows that allow accurate and timely measurement of the SDIs from sub-national to global scales is proving challenging. We discuss a research program that aims to develop techniques to meet these challenges and help provide member states of the UN with effective methods of monitoring progress towards meeting the goals of the 2030 Agenda for Sustainable Development.

Back to AI for Social Good event

Using AI and Satellite Earth Observation to Monitor UN Sustainable Development Indicators

Pages