Data science for social equality
Abstract/Contents
- Abstract
- Recent work in algorithmic fairness has highlighted the ways in which machine learning and data science can exacerbate already profound social inequalities. While invaluable, this work should not cause us to lose sight of the more optimistic counterpoint: that machine learning and data science have the potential to also reduce social inequality if properly applied. This dissertation explores this potential. In the first half of the dissertation, we provide two examples illustrating how data science and machine learning can improve healthcare for underserved populations. We first develop a deep learning algorithm which identifies pain-relevant features in knee osteoarthritis x-rays which conventional severity measures overlook, but which help explain higher pain levels in black, lower-income, and lower-education patients. Secondly, we use data from a women's health app to decompose women's mood, behavior, and vital signs into four simultaneous cycles --- daily, weekly, seasonal, and menstrual --- and reveal that the menstrual cycle, though often invisible in past analyses, is the largest of the four cycles. In the second half of the dissertation, we provide two examples illustrating how data science and machine learning can detect bias in human decision-making, focusing on policing as an application domain. We first describe a new family of probability distributions and use them to accelerate a Bayesian test for discrimination by two orders of magnitude, allowing it to scale to much larger datasets. We then apply this test to a national dataset of traffic stops which we collect via public records requests and publicly release. The methods we develop are more broadly applicable to assessing bias in many other human decisions
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Pierson, Emma |
|---|---|
| Degree supervisor | Leskovec, Jure |
| Thesis advisor | Leskovec, Jure |
| Thesis advisor | Jurafsky, Dan |
| Thesis advisor | Zou, James |
| Degree committee member | Jurafsky, Dan |
| Degree committee member | Zou, James |
| Associated with | Stanford University, Computer Science Department. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Emma Pierson |
|---|---|
| Note | Submitted to the Computer Science Department |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Emma Pierson
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...