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Abstract Evaluating the social impacts of engineered products is critical to ensuring that products are having their intended positive impacts and learning how to improve product designs for a more positive social impact. Quantitative evaluation of product social impacts is made possible through the use of social impact indicators, which combine the user data in a meaningful way to give insight into the current social condition of an individual or population. Most existing methods for collecting these user data for social impact indicators require direct human interaction with users of a product (e.g., interviews, surveys, and observational studies). These interactions produce high-fidelity data that help indicate the product impact but only at a single snapshot in time and are typically infrequently collected due to the large human resources and cost associated with obtaining them. In this article, a framework is proposed that outlines how low-fidelity data often obtainable using remote sensors, satellites, or digital technology can be collected and correlated with high-fidelity, infrequently collected data to enable continuous, remote monitoring of engineered products via the user data. These user data are critical to determining current social impact indicators that can be used in a posteriori social impact evaluation. We illustrate an application of this framework by demonstrating how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda. Key to this example is the use of a deep learning model to correlate user type (man, woman, or child statured) with the raw hand pump data obtained via an integrated motion unit sensor for 1200 hand pump users. 

Abstract Those working in Engineering for Global Development seek to improve the conditions in developing countries. A common metric for understanding the development state of a given country is the Human Development Index (HDI), which focuses on three dimensions: health, education, and income. An engineer’s expertise does not always align with any of those dimensions directly, while they still hope to perform impactful work for human development. To discover other areas of expertise that are highly associated with the HDI, correlations and variable selection were performed between all World Development Indicators and the HDI. The resultant associations are presented according to industry sector for a straightforward connection to engineering expertise. The associated areas of expertise can be used during opportunity development as surrogates for focusing on the HDI dimensions themselves. The data analysis shows that work related to “Trade, Transportation, and Utilities,” such as electricity distribution, and exports or imports, “Natural Resources and Mining,” such as energy resources, agriculture, or access to clean water, and “Manufacturing,” in general, are most commonly associated with improvements in the HDI in developing countries. Also, because the associations were discovered at country-level, they direct where geographically particular areas of expertise have been historically associated with improving HDI.