2 research outputs found
Monitoring Sustainable Global Development Along Shared Socioeconomic Pathways
Sustainable global development is one of the most prevalent challenges facing
the world today, hinging on the equilibrium between socioeconomic growth and
environmental sustainability. We propose approaches to monitor and quantify
sustainable development along the Shared Socioeconomic Pathways (SSPs),
including mathematically derived scoring algorithms, and machine learning
methods. These integrate socioeconomic and environmental datasets, to produce
an interpretable metric for SSP alignment. An initial study demonstrates
promising results, laying the groundwork for the application of different
methods to the monitoring of sustainable global development.Comment: 5 pages, 1 figure. Presented at NeurIPS 2023 Workshop: Tackling
Climate Change with Machine Learnin
TraCE: Trajectory Counterfactual Explanation Scores
Counterfactual explanations, and their associated algorithmic recourse, are
typically leveraged to understand, explain, and potentially alter a prediction
coming from a black-box classifier. In this paper, we propose to extend the use
of counterfactuals to evaluate progress in sequential decision making tasks. To
this end, we introduce a model-agnostic modular framework, TraCE (Trajectory
Counterfactual Explanation) scores, which is able to distill and condense
progress in highly complex scenarios into a single value. We demonstrate
TraCE's utility across domains by showcasing its main properties in two case
studies spanning healthcare and climate change.Comment: 7 pages, 4 figures, appendi