Welcome to the Machine: Ir/Responsible Use of Machine Learning in Research Recommendation Tools
| Metadata Field | Value | Language |
|---|---|---|
| dc.contributor | Ali Krzton, alk0043@auburn.edu | en_US |
| dc.creator | Krzton, Ali | |
| dc.date.accessioned | 2023-03-17T21:44:21Z | |
| dc.date.available | 2023-03-17T21:44:21Z | |
| dc.date.created | 2023-03-17 | |
| dc.identifier.uri | https://aurora.auburn.edu/handle/11200/50507 | |
| dc.identifier.uri | http://dx.doi.org/10.35099/aurora-575 | |
| dc.description.abstract | Machine learning is changing how researchers interact with scholarly literature. While it has the potential to reveal exciting new connections between areas of study, popular commercial tools that provide recommendations to users based on personalized profiles developed with machine learning can negatively impact the breadth of sources they will discover. Over time, they may even limit the questions researchers are willing to explore. Find out what machine learning is and how it is being deployed to make the literature more manageable, then learn strategies to warn users about its pitfalls and advocate for the responsible use of this technology. | en_US |
| dc.format | .pptx | en_US |
| dc.publisher | Association of College & Research Libraries | en_US |
| dc.relation.ispartof | ACRL 2023 | en_US |
| dc.rights | Creative Commons Attribution 4.0 International License (CC-BY) | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | artificial intelligence | en_US |
| dc.subject | scholarly communication | en_US |
| dc.subject | user profiling | en_US |
| dc.title | Welcome to the Machine: Ir/Responsible Use of Machine Learning in Research Recommendation Tools | en_US |
| dc.type | Text | en_US |
| dc.type.genre | Presentation, Paper Presentation | en_US |
| dc.description.peerreview | No | en_US |
| dc.creator.alternate | Krzton, Alicia | |
| dc.location | Pittsburgh, PA | en_US |
| dc.creator.orcid | 0000-0001-9979-2471 | en_US |