Teaching Algorithmic Bias in a Credit-Bearing Course
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- Titel
- Teaching Algorithmic Bias in a Credit-Bearing Course
- verantwortlich
- Erscheinungsjahr
- 2019
- Medientyp
- Preprint
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- LISSA
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Carolyn Gardner Carolyn Gardner |
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Carolyn Gardner |
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Carolyn Gardner Teaching Algorithmic Bias in a Credit-Bearing Course Social and Behavioral Sciences evaluating information academic libraries bepress LIS Scholarship Archive information literacy Information Literacy Library and Information Science algorithmic bias |
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carolyn gardner |
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Carolyn Gardner Social and Behavioral Sciences evaluating information academic libraries bepress LIS Scholarship Archive information literacy Information Literacy Library and Information Science algorithmic bias http://osf.io/cnb4h/ http://dx.doi.org/10.31229/OSF.IO/CNB4H Information literacy instruction has become increasingly nuanced with the widespread adoption of critical approaches to teaching and the ACRL Framework. Librarians are already teaching information evaluation strategies, however, more work can be done in the area of understanding algorithmic decision making and bias. This column describes how a public university integrated lessons on algorithmic bias into a credit-bearing information literacy course for a general undergraduate audience. The activities and readings can be adapted to a one-shot instruction environment and the collaborative process for designing the curriculum is also shared. Teaching Algorithmic Bias in a Credit-Bearing Course |
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| title |
Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_unstemmed |
Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_full |
Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_fullStr |
Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_full_unstemmed |
Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_short |
Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_sort |
teaching algorithmic bias in a credit-bearing course |
| topic |
Social and Behavioral Sciences evaluating information academic libraries bepress LIS Scholarship Archive information literacy Information Literacy Library and Information Science algorithmic bias |
| url |
http://osf.io/cnb4h/ http://dx.doi.org/10.31229/OSF.IO/CNB4H |
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2019 |
| physical |
|
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Information literacy instruction has become increasingly nuanced with the widespread adoption of critical approaches to teaching and the ACRL Framework. Librarians are already teaching information evaluation strategies, however, more work can be done in the area of understanding algorithmic decision making and bias. This column describes how a public university integrated lessons on algorithmic bias into a credit-bearing information literacy course for a general undergraduate audience. The activities and readings can be adapted to a one-shot instruction environment and the collaborative process for designing the curriculum is also shared. |
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| author | Carolyn Gardner |
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| description | Information literacy instruction has become increasingly nuanced with the widespread adoption of critical approaches to teaching and the ACRL Framework. Librarians are already teaching information evaluation strategies, however, more work can be done in the area of understanding algorithmic decision making and bias. This column describes how a public university integrated lessons on algorithmic bias into a credit-bearing information literacy course for a general undergraduate audience. The activities and readings can be adapted to a one-shot instruction environment and the collaborative process for designing the curriculum is also shared. |
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| spelling | Carolyn Gardner Social and Behavioral Sciences evaluating information academic libraries bepress LIS Scholarship Archive information literacy Information Literacy Library and Information Science algorithmic bias http://osf.io/cnb4h/ http://dx.doi.org/10.31229/OSF.IO/CNB4H Information literacy instruction has become increasingly nuanced with the widespread adoption of critical approaches to teaching and the ACRL Framework. Librarians are already teaching information evaluation strategies, however, more work can be done in the area of understanding algorithmic decision making and bias. This column describes how a public university integrated lessons on algorithmic bias into a credit-bearing information literacy course for a general undergraduate audience. The activities and readings can be adapted to a one-shot instruction environment and the collaborative process for designing the curriculum is also shared. Teaching Algorithmic Bias in a Credit-Bearing Course |
| spellingShingle | Carolyn Gardner, Teaching Algorithmic Bias in a Credit-Bearing Course, Social and Behavioral Sciences, evaluating information, academic libraries, bepress, LIS Scholarship Archive, information literacy, Information Literacy, Library and Information Science, algorithmic bias |
| title | Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_full | Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_fullStr | Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_full_unstemmed | Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_short | Teaching Algorithmic Bias in a Credit-Bearing Course |
| title_sort | teaching algorithmic bias in a credit-bearing course |
| title_unstemmed | Teaching Algorithmic Bias in a Credit-Bearing Course |
| topic | Social and Behavioral Sciences, evaluating information, academic libraries, bepress, LIS Scholarship Archive, information literacy, Information Literacy, Library and Information Science, algorithmic bias |
| url | http://osf.io/cnb4h/, http://dx.doi.org/10.31229/OSF.IO/CNB4H |