Security For Databases and Throughout Computer Science Curriculum; An Analysis of Machine Learning and Artificial Intelligence on Climate Change Through Capitalism

Padilla Coo, Franceska, School of Engineering and Applied Science, University of Virginia
Basit, Nada, EN-Comp Science Dept, University of Virginia
Francisco, Pedro Augusto, EN-Engineering and Society, University of Virginia

Awareness about machine learning and artificial intelligence’s (AI) contributions to the climate crisis has not been keeping pace with climate change’s rapid progression. I seek to examine why this information is not more commonly spread and taught from a capitalist lens using the black box theory in order to pinpoint specific areas of improvement. The main reasons for this lack of information dissemination include the lack of education behind sustainable computing, the current importance and value placed on machine learning and AI, and the perception that switching over to sustainable options is time consuming and economically harmful. The different reasons I provide have been provided by other scholars, but I go one more step to connect these reasons together and talk about their interplay within the capitalist system. With machine learning and AI making significant contributions to climate change and its rapid integration into many businesses, the motivations behind the lack of action in this area of computer science must be addressed.

BS (Bachelor of Science)
machine learning, capitalism, climate change

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Nada Basit

STS Advisor: Pedro Augusto Francisco

Technical Team Members: Noah Cook

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