Model interpretation and data valuation for machine learning
Abstract/Contents
- Abstract
- Machine learning is being applied in various critical applications like healthcare. In order to be able to trust a machine learning model and to repair it once it malfunctions, it is important to be able to interpret its decision-making. For example, if a model's performance is poor on a specific subgroup (gender, race, etc), it is important to find out why and fix it. In this thesis, we examine the drawbacks of existing interpretability methods and introduce new ML interpretability algorithms that are designed to tackle some of the shortcomings. Data is the labor that trains machine learning models. It is not possible to interpret an ML model's behavior without going back to the data that trained it in the first place. A fundamental challenge is how to quantify the contribution of each source of data to the model's performance. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual datum. In this thesis, we discuss principled frameworks for equitable \emph{valuation} of data; that is, given a learning algorithm and a performance metric that quantifies the performance of the resulting model, we try to find the contribution of individual datum. This thesis is divided in 3 sections, machine learning interpretability and fairness, data valuation, and machine learning for healthcare - all linked by the common goal of making the use of machine learning more responsible for the benefit of human beings.
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 | Ghorbani, Amirata |
|---|---|
| Degree supervisor | Zou, James |
| Thesis advisor | Zou, James |
| Thesis advisor | Pauly, John (John M.) |
| Thesis advisor | Weissman, Tsachy |
| Degree committee member | Pauly, John (John M.) |
| Degree committee member | Weissman, Tsachy |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Amirata Ghorbani. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/dj449dg7488 |
Access conditions
- Copyright
- © 2021 by Amirata Ghorbani
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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