Fairness in algorithmic services
Abstract/Contents
- Abstract
- Algorithmically guided decisions are becoming increasingly prevalent and, if left unchecked, can amplify pre-existing societal biases. This thesis employs modern computational tools to examine the equity of decision-making in two complex systems: automated speech recognition and online advertising. Firstly, I demonstrate large racial disparities in the performance of popular commercial speech-to-text systems developed by Amazon, Apple, Google, IBM, and Microsoft, a pattern likely stemming from a lack of diversity in the data used to train the systems. These results point to hurdles faced by African Americans in using widespread tools driven by speech recognition technology. Secondly, I propose a methodological framework for online advertisers to determine a demographically equitable allocation of individuals being shown ads for SNAP (food stamp) benefits. In particular, I discuss how to formulate fair decisions considering budget-constrained trade-offs between English-speaking and Spanish-speaking SNAP applicants. Both application domains exemplify processes to ameliorate demographic-based disparate impact arising from decisions made by online platforms, with the ultimate goal of uplifting underserved communities.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Koenecke, Allison |
|---|---|
| Degree supervisor | Athey, Susan |
| Degree supervisor | Goel, Sharad, 1977- |
| Thesis advisor | Athey, Susan |
| Thesis advisor | Goel, Sharad, 1977- |
| Thesis advisor | Varian, Hal R |
| Degree committee member | Varian, Hal R |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Allison Koenecke. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/gc277qj5340 |
Access conditions
- Copyright
- © 2021 by Allison Koenecke
- 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...