Poverty maps derived from satellite imagery are increasingly used to inform
high-stakes policy decisions, such as the allocation of humanitarian aid and
the distribution of government resources. Such poverty maps are typically
constructed by training machine learning algorithms on a relatively modest
amount of ``ground truth" data from surveys, and then predicting poverty levels
in areas where imagery exists but surveys do not. Using survey and satellite
data from ten countries, this paper investigates disparities in representation,
systematic biases in prediction errors, and fairness concerns in
satellite-based poverty mapping across urban and rural lines, and shows how
these phenomena affect the validity of policies based on predicted maps. Our
findings highlight the importance of careful error and bias analysis before
using satellite-based poverty maps in real-world policy decisions.

本文通过使用十个国家的调查与卫星数据，研究城乡界限上的贫困地图建立中的不平等现象、预测误差中的系统性偏差以及公平性问题，展示了这些现象如何影响基于预测地图的政策的有效性。本文的发现强调了在使用卫星贫困地图做出现实世界政策决策之前进行仔细的误差和偏差分析的重要性。

卫星贫困地图中的公平性和代表性：城乡差异及其对下游政策的影响证据

Fairness and representation in satellite-based poverty maps: Evidence of  urban-rural disparities and their impacts on downstream policy

International trade policies have recently garnered attention for limiting
cross-border exchange of essential goods (e.g. steel, aluminum, soybeans, and
beef). Since trade critically affects employment and wages, predicting future
patterns of trade is a high-priority for policy makers around the world. While
traditional economic models aim to be reliable predictors, we consider the
possibility that Machine Learning (ML) techniques allow for better predictions
to inform policy decisions. Open-government data provide the fuel to power the
algorithms that can explain and forecast trade flows to inform policies. Data
collected in this article describe international trade transactions and
commonly associated economic factors. Machine learning (ML) models deployed
include: ARIMA, GBoosting, XGBoosting, and LightGBM for predicting future trade
patterns, and K-Means clustering of countries according to economic factors.
Unlike short-term and subjective (straight-line) projections and medium-term
(aggre-gated) projections, ML methods provide a range of data-driven and
interpretable projections for individual commodities. Models, their results,
and policies are introduced and evaluated for prediction quality.

本研究介绍了使用机器学习算法，基于国际贸易交易和相关经济因素，进行预测和解释交易流的可能性，并对预测结果和政策决策进行了评估。

机器学习在国际贸易趋势预测中的应用

Application of Machine Learning in Forecasting International Trade  Trends

Life-expectancy is a complex outcome driven by genetic, socio-demographic,
environmental and geographic factors. Increasing socio-economic and health
disparities in the United States are propagating the longevity-gap, making it a
cause for concern. Earlier studies have probed individual factors but an
integrated picture to reveal quantifiable actions has been missing. There is a
growing concern about a further widening of healthcare inequality caused by
Artificial Intelligence (AI) due to differential access to AI-driven services.
Hence, it is imperative to explore and exploit the potential of AI for
illuminating biases and enabling transparent policy decisions for positive
social and health impact. In this work, we reveal actionable interventions for
decreasing the longevity-gap in the United States by analyzing a County-level
data resource containing healthcare, socio-economic, behavioral, education and
demographic features. We learn an ensemble-averaged structure, draw inferences
using the joint probability distribution and extend it to a Bayesian Decision
Network for identifying policy actions. We draw quantitative estimates for the
impact of diversity, preventive-care quality and stable-families within the
unified framework of our decision network. Finally, we make this analysis and
dashboard available as an interactive web-application for enabling users and
policy-makers to validate our reported findings and to explore the impact of
ones beyond reported in this work.

本研究通过分析县级数据资源，实现了一个可交互的在线应用程序来减少美国长寿差距，提出了减少差距的可行干预措施，并通过贝叶斯决策网络进行决策，利用人工智能发现偏见并制定透明的决策，为消除与健康相关的不平等提供了可行的方案。