Identifying and Mitigating Bias in Machine Learning Applications
| dc.contributor.author | Bald, Laura | |
| dc.date.accessioned | 2019-07-17T22:43:41Z | |
| dc.date.available | 2019-07-17T22:43:41Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | This study addresses the existence of bias in machine learning applications and examines techniques for identifying and mitigating bias using scholarly literature published between 2012 and 2019. The intended audience is machine learning engineers, system analysts, and data analysts of any industry. This study is significant because there may be considerable ethical implications caused by machine learning bias; identifying and mitigating these biases is key to the development and deployment of effective machine learning algorithms. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/24785 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Oregon | en_US |
| dc.relation.ispartofseries | AIM Capstone;Bald2019 | |
| dc.rights | Creative Commons BY-NC-ND 4.0-US | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Artificial intelligence | en_US |
| dc.subject | Bias | en_US |
| dc.subject | Variance | en_US |
| dc.subject | Machine bias | en_US |
| dc.subject | Ethics | en_US |
| dc.subject | Algorithmic bias | en_US |
| dc.subject | Machine learning models | en_US |
| dc.title | Identifying and Mitigating Bias in Machine Learning Applications | en_US |
| dc.type | Terminal Project | en_US |