An Analysis of Recommendation Methods in Movie Recommendations ; Face Recognition: A Struggle between Security, Convenience, Privacy, and Equity

How to gain trust in modern AI and Machine Learning technologies that raise
questions from the general public? It is necessary to demonstrate how the process works
and why it works the way it does.
State-of-the-art methods for recommendation systems are constantly being
developed and improved. Analyzing different implementations and testing them on real
data provides insight into how those algorithms learn from the data and specific factors
that determine what gets recommended. This project used a dataset of movies from
MovieLens to compare different algorithms. It is important to understand how different
recommendation algorithms work to determine what works best and what could be
improved. In future studies, using different and diverse datasets, as well as other
algorithms can further improve the understanding of generated recommendations.
In the U.S. since 2010, how have social groups competed to draw the line
between necessary building security and invasive building security? Privacy is a major
concern for many living in the U.S. and with modern technology it became much easier
to compromise. Social groups have started using technologies and sophisticated
algorithms that follow security protocols while also adhering to privacy concerns of the
general public. Regulations are put in place to limit data storage for technology such as
facial recognition and AI security companies promote and monitor ethical usage of their
software. Facial recognition and similar modern technologies have many benefits, but
should also be regulated to protect the rights of the people.

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Hongning Wang
STS Advisor: Peter Norton
Technical Team Members:
Nikita Semichev