The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it.
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Health Affairs (Project Hope)
2022
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| Online Access: | https://hdl.handle.net/1721.1/143894 |
| _version_ | 1811078696676622336 |
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| author | Gervasi, Stephanie S Chen, Irene Y Smith-McLallen, Aaron Sontag, David Obermeyer, Ziad Vennera, Michael Chawla, Ravi |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Gervasi, Stephanie S Chen, Irene Y Smith-McLallen, Aaron Sontag, David Obermeyer, Ziad Vennera, Michael Chawla, Ravi |
| author_sort | Gervasi, Stephanie S |
| collection | MIT |
| first_indexed | 2024-09-23T11:04:02Z |
| format | Article |
| id | mit-1721.1/143894 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:04:02Z |
| publishDate | 2022 |
| publisher | Health Affairs (Project Hope) |
| record_format | dspace |
| spelling | mit-1721.1/1438942023-02-09T15:57:59Z The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it. Gervasi, Stephanie S Chen, Irene Y Smith-McLallen, Aaron Sontag, David Obermeyer, Ziad Vennera, Michael Chawla, Ravi Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 2022-07-20T16:38:14Z 2022-07-20T16:38:14Z 2022 2022-07-20T16:22:57Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/143894 Gervasi, Stephanie S, Chen, Irene Y, Smith-McLallen, Aaron, Sontag, David, Obermeyer, Ziad et al. 2022. "The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it.." Health Affairs, 41 (2). en 10.1377/HLTHAFF.2021.01287 Health Affairs Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Health Affairs (Project Hope) Health Affairs |
| spellingShingle | Gervasi, Stephanie S Chen, Irene Y Smith-McLallen, Aaron Sontag, David Obermeyer, Ziad Vennera, Michael Chawla, Ravi The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it. |
| title | The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it. |
| title_full | The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it. |
| title_fullStr | The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it. |
| title_full_unstemmed | The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it. |
| title_short | The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it. |
| title_sort | potential for bias in machine learning and opportunities for health insurers to address it article examines the potential for bias in machine learning and opportunities for health insurers to address it |
| url | https://hdl.handle.net/1721.1/143894 |
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