Abstract
Evaluating the social impact indicators of engineered products is crucial to better understanding how products affect individuals’ lives and discover how to design for positive social impact. Most existing methods for evaluating social impact indicators require direct human interaction with users of a product, such as one-on-one interviews. These interactions produce high-fidelity data that are rich in information but provide only a single snapshot in time of the product’s impacts and are less frequently collected due to the significant human resources and cost associated with obtaining them. A framework is proposed that describes how low-fidelity data passively obtained using remote sensors, satellites, and digital technology can be collected and correlated with high-fidelity, low-frequency data using machine learning. Using this framework provides an inexpensive way to continuously monitor the social impact indicators of products by augmenting high-fidelity, low-frequency data with low-fidelity, continuously-collected data using machine learning. We illustrate an application of this framework by demonstrating how it can be used to examine the gender-related social impact indicators of water pumps in Uganda. The provided example uses a deep learning model to correlate pump handle movement (measured via an integrated motion unit) with user type (man, woman, or child) of 1,200 hand pump users.
Nonparametric estimation of scalar diffusions based on low frequency data
