Location-Wise: A Mobile Application That Displays Real-Time Traffic of Locations Using Geolocation Data; An Analysis of the Prevalence and Correction of Biases in Financial Institutions

Lindquist, Maxwell, School of Engineering and Applied Science, University of Virginia
Cohoon, Jim, EN-Comp Science Dept, University of Virginia
Bloomfield, Aaron, University of Virginia
Baritaud, Catherine, University of Virginia
Seabrook, Bryn, University of Virginia

The COVID-19 pandemic produced worldwide devastation on multiple fronts. Public health and financial emergencies arose as a result of the COVID-19 virus. Through the creation of a mobile application, the technical topic addresses the ability to access resources. These resources, such as food, medicine, and hygiene products, are necessary for comfortable survival, yet difficult to safely access during the pandemic. The science, technology, and society (STS) paper focuses on the impact of biased financial practices on a variety of social groups. The technical project and STS research address similar themes of resource access in modern times. Potential solutions to ensure equitable accessibility are proposed by both the technical project and STS research.
Information about the COVID-19 virus continues to evolve as we learn more about the disease. Recent studies have shown that the virus spreads most effectively in crowded, indoor areas. Locations, such as grocery stores, pharmacies, and gas stations are often crowded, presenting potentially dangerous environments for customers. The prototype application of the technical report is designed to mitigate these dangers. The mobile application created by the capstone team provides users a way to quickly evaluate the safety of businesses. This sample Android application implements several key features of the overall system design, such as the ability to view business crowd levels.
The prototype design provides a glimpse of the user experience of the application. While key functionalities of the application were implemented, time and resource constraints limited the scope of development. A blueprint was provided for the remaining functionalities of the application. Safe data storage techniques were researched, such as the collection of user data in a temporary special database. Despite the limited scope of application development, reported user experiences remained positive. The functionality and interface design were seen as improvements from existing applications.
The STS research addresses a similar access to resources, specifically financial resources. New technologies, such as artificial intelligence (AI) and machine learning (ML), are becoming increasingly common in the financial industry. Their rise in popularity raises the question: How can fairness in new technology be audited? Smith and Marx’s interpretation of technological determinism was applied to evaluate the impact of biased financial decisions on consumers, corporations, and policymakers. Legislation, such as the Civil Rights Act of 1964 and Fair Housing Act, were researched to evaluate constraints surrounding these technologies.
Upon investigation, it became evident that ethical guidelines surrounding new technologies remains limited. This lack of regulation is often attributed to recent technological failures, such as the Michigan Integrated Data Automated System. The long-lasting implications of these failures, as highlighted through the lens of technological determinism, impact a variety of social groups. These lasting impacts have sparked a recent push in ethical guidelines, such as broad legislation proposed by European countries. While AI regulation remains limited, new efforts are being made by both policymakers and corporations to improve technological practices.
The technical project and STS research identify ways to increase the accessibility of necessary resources. While righting the wrongs of past inequities is a daunting task, these projects serve as a step in the right direction to improve accessibility in the future.

BS (Bachelor of Science)
Artificial Intelligence, Machine Learning, Technological Determinism, Bias

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisors: Jim Cohoon; Aaron Bloomfield
STS Advisors: Catherine Baritaud, Bryn Seabrook
Technical Team Members: Shannon Chu

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