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Abstract Meeting the United Nations (UN) sustainable development goals efficiently requires designers and engineers to solve multi-objective optimization problems involving trade-offs between social, environmental, and economical impacts. This paper presents an approach for designers and engineers to quantify the social and environmental impacts of a product at a population level and then perform a trade-off analysis between those impacts. In this approach, designers and engineers define the attributes of the product as well as the materials and processes used in the product’s life cycle. Agent-based modeling (ABM) tools that have been developed to model the social impacts of products are combined with life cycle assessment (LCA) tools that have been developed to evaluate the pressures that different processes create on the environment. Designers and engineers then evaluate the trade-offs between impacts by finding non-dominated solutions that minimize environmental impacts while maximizing positive and/or minimizing negative social impacts. Product adoption models generated by ABM allow designers and engineers to approximate population level environmental impacts and avoid Simpson’s paradox, where a reversal in choices is preferred when looking at the population level impacts versus the individual product-level impacts. This analysis of impacts has the potential to help designers and engineers create more impactful products that aid in reaching the UN sustainable development goals. 

Participatory design approaches such as co-design are promoted as ways to increase the likelihood that engineered products are economically, environmentally, and socially sustainable by incorporating stakeholders into decision-making processes. However, executing collaborative design practices that incorporate the variety of stakeholders represents an enormous challenge. In this paper we examine these realities as experienced by a co-design team comprised of design engineers from a foreign country who are engaged with local stakeholders to develop a product for a community in the Brazilian Amazon. Based on more than a year of ethnographic research, we identify three types of perspectives or institutional logics operating in this setting—engineering, modernization, and traditional—which interact to constrain and enable the co-design process. We find that these logics can undermine co-design because the design team is better equipped to respond to stakeholders who express modernization logics rather than traditional ones. We conclude that while co-design can be truly collaborative in development projects, other times it may lead to the appearance that the design process is collaborative when it may in fact mask the marginalization of certain stakeholder voices. 

Abstract While many tools and methodologies for assessing social impact exist and are used in the social science and global development fields, there is a lack of standard methods for considering the broader social impact of products in the engineering community. Some reasons these methods are not as widely used in the engineering community include designers not being aware of the methods, or methods not being widely applicable. The purpose of this research is to help designers and researchers find relevant design tools and methods for implementing social impact considerations. This is done through the classification of 374 papers in the Engineering for Global Development (EGD) literature along several dimensions including method purpose, industry sector, social impacts considered, sustainable development goals, paper setting, and data inputs required. This article describes how designers and researchers can use this set of classified papers to locate relevant design tools and methods to improve social impact considerations in their work. 

With limited time and resources available to carry out Engineering for Global Development (EGD) projects, it can be difficult to know where those resources should be allocated to have greater potential for meaningful impact. It is easy to assume that projects should occur in a particular location based on personal experience or where other development projects are taking place. This can be a consideration, but it may not lead to the greatest social impact. Where to work on a project and what problem to work on are key questions in the early stages of product development in the context of EGD. To aid in this process, this article presents a method for assessing global needs to ensure thoughtful use of limited EGD resources. We introduce a method for identifying locations where there is human need, gaps in technological achievement, and what the work environment is in a country. Results of the method are compared to what countries receive the most foreign aid dollars per capita. Measures were calculated using the principal component analysis on data from development agencies. These results can help practitioners in selecting where to undertake development projects with an eye toward targeting locations that may yield high levels of social impact. 

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 Engineered products often have more social impacts than are realized. A product review was conducted to bring this to light. In this paper, we show the extent to which different social impacts in 11 impact categories are co-present in 150 products and how this can help engineers and others during the product development process. Specifically, we show how social impact categories not previously considered can be identified. The product review resulted in 13,200 data points that were divided into two data sets, one with 8800 data points from which a social impact probability table was created. The remaining data points were then used to validate the table. All data points were then combined to create a final social impact probability table. This table provides insight for how various social impact categories correlate and can assist engineers in expanding their views to include additional social impact objectives and thus achieve a design with broader social impact or a design with minimized unwanted negative social impact. A simple method for predicting social impact is also created in order to assist engineers when developing products with social impacts in mind. 

Abstract All products impact the lives of their users, this is called social impact. Some social impacts are commonly recognized by the engineering community, such as impacts to a user’s health and safety, while other social impacts can be more difficult to recognize, such as impacts on families and gender roles. When engineers make design decisions, without considering social impacts, they can unknowingly cause negative social impacts. Even harming the user and/or society. Despite its challenges, measuring a program’s or policy’s social impact is a common practice in the field of social sciences. These measurements are made using social impact indicators, which are simply the things observed to verify that true progress is being made. While there are clear benefits to predicting the social impact of an engineered product, it is unclear how engineers should select indicators and build predictive social impact models that are functions of engineering parameters and decisions. This paper introduces a method for selecting social impact indicators and creating predictive social impact models that can help engineers predict and improve the social impact of their products. As a first step in the method, an engineer identifies the product’s users, objectives, and requirements. Then, the social impact categories that are related to the product are determined. From each of these categories, the engineer selects several social impact indicators. Finally, models are created for each indicator to predict how a product’s parameters will change these indicators. The impact categories and indicators can be translated into product requirements and performance measures that can be used in product development processes. This method is used to predict the social impact of the proposed, expanded U.S. Mexico border wall.