*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Human Aspects of Machine Learning
Samira Samadi
School of Computer Science
College of Computing
Georgia Institute of Technology
Date: Thursday, March 5th, 2020
Time: 11:00 am - 12:30 pm (EST)
Location: Klaus 1315
Committee:
Dr. Santosh Vempala (Advisor, School of Computer Science, Georgia Institute of Technology)
Dr. Adam Kalai (Senior Principal Researcher, Microsoft Research New England)
Dr. Jamie Morgenstern (School of Computer Science & Engineering, University of Washington)
Dr. Vivek Sarkar (School of Computer Science, Georgia Institute of Technology)
Dr. Mohit Singh (School of Industrial & Systems Engineering, Georgia Institute of Technology)
Abstract:
As humans are inevitably being influenced by machine learning algorithms, it is crucial to study the human aspects of these algorithms. In my research, I investigate several ML paradigms from the viewpoint of human usability, security, and fairness.
In the first line of work, I study human usability and security of password strategies — mental algorithms proposed by Blum and Vempala to help people calculate, in their heads, passwords for different websites without dependence on external devices. I present the first usability study of two password strategies: the 3-word strategy and the letter-code strategy. Furthermore, I show that given a limited amount of memorization, there are humanly usable password strategies that achieve the information-theoretic highest security guarantee.
In the second line of work, I investigate different fairness criteria for several machine learning techniques including principal component analysis (PCA) and spectral clustering. I show on real-world data sets that PCA can inadvertently produce low-dimensional representations with different fidelity for two different populations (e.g., lighter- versus darker-skin tone individuals). I define the notion of Fair PCA and present an efficient algorithm for finding a low-dimensional representation of the data which is nearly-optimal for this measure. I conclude by a study of spectral clustering with the constraint that every demographic is proportionally represented in each cluster. For this goal, I develop variants of constrained spectral clustering and show that they help find fairer clusterings on real data.
