Software Development: Creating a Student Planner Application with UVA Course Data; The Effect of Different Social Groups on the Development of Artificial Intelligence

Aceron, Justin, School of Engineering and Applied Science, University of Virginia
Francisco, Pedro Augusto, EN-Engineering and Society, University of Virginia
Morrison, Briana, EN-Comp Science Dept, University of Virginia

My originally proposed capstone research addresses the limitations of current methods for detecting early symptoms of heart failure and the potential benefits of using alternative prediction and detection methods. Leveraging machine learning and regression algorithms, there’s potential to develop predictive models that utilize biometric data to assess the likelihood of heart disease in patients. It’s important to consider the human and social aspects of incorporating such technology since these predictive models can influence human lives; the models have to be accurate and trusted by the humans using them if we are to seriously consider including AI in the health industry. For the STS research pertaining to how AI has developed, it’s important to consider the Social Construction of Technology theoretical framework. Considering a wide range of different stakeholders other than the intended users will be important in identifying what makes certain AI applications successful. Historical analysis of secondary sources is the main method of research for the STS project. Close examination of government policies related to AI also proves to be useful in finding the social impacts that various groups have on the development of AI over time. Through the STS research, I hope to find out how different social groups interpret AI technology while also looking into how current developers design their AI. This may provide insight into the complex number of social factors that have to be considered for successful AI applications to be used by the public. In tandem, the STS research and the capstone project exemplify how there are many potential benefits of AI, but only if developers make the effort to consider AI’s dynamic social contexts when creating such technology.

BS (Bachelor of Science)
Artificial Intelligence, Software Development, Application Design, Social Construction of Technology

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Briana Morrison
STS Advisor: Pedro A. P. Francisco
Technical Team Members: Justin Aceron

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