Model Evaluation Service: Improving Machine Learning Model Development Efficiency / The Struggle over Digital Privacy in the United States
Author: 
orcid.org/0009-0003-9603-1580
Kwong, Alex, School of Engineering and Applied Science, University of Virginia
orcid.org/0009-0003-9603-1580Kwong, Alex, School of Engineering and Applied Science, University of Virginia
Advisor:
Vrugtman, Rosanne, EN-Comp Science Dept, University of Virginia
Vrugtman, Rosanne, EN-Comp Science Dept, University of Virginia
Abstract:
How can machine learning improve productivity in organizations? Recent developments
in machine learning (ML) offer new possibilities in automation that may transform numerous
economic sectors and boost organizational efficiency. Yet ML also threatens to accelerate
problematic trends, including invasion of personal privacy, unimpeded user data collection and
monetization, and propagation of targeted misinformation.
Degree:
BS (Bachelor of Science)
BS (Bachelor of Science)
Keywords:
machine learning, data privacy, big data, digital privacy
machine learning, data privacy, big data, digital privacy
Language:
English
English
Issued Date:
2023/06/07
2023/06/07
