Teaching Machine Learning in the Context of Critical Quantitative Information Literacy

Carrie Diaz Eaton
Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 170:51-56, 2022.

Abstract

Bates College, is a small liberal arts postsecondary institution in the northeast United States. An information literacy course, Calling Bull, serves as an introductory data science class as well as a prerequisite-free quantitative literacy class. In this context, we spend a week discussing machine learning, with an emphasis on facial recognition algorithms. The emphasis is on the general algorithmic approach, critical inquiry of the process and careful interpretation of results presented in research or decision-making. This module relies on the use of open educational materials, discussion, and careful attention to issues of marginalization and algorithmic justice.

Cite this Paper

BibTeX
@InProceedings{pmlr-v170-eaton22a, title = {Teaching Machine Learning in the Context of Critical Quantitative Information Literacy}, author = {Eaton, Carrie Diaz}, booktitle = {Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {51--56}, year = {2022}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {170}, series = {Proceedings of Machine Learning Research}, month = {08--13 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v170/eaton22a/eaton22a.pdf}, url = {https://proceedings.mlr.press/v170/eaton22a.html}, abstract = {Bates College, is a small liberal arts postsecondary institution in the northeast United States. An information literacy course, Calling Bull, serves as an introductory data science class as well as a prerequisite-free quantitative literacy class. In this context, we spend a week discussing machine learning, with an emphasis on facial recognition algorithms. The emphasis is on the general algorithmic approach, critical inquiry of the process and careful interpretation of results presented in research or decision-making. This module relies on the use of open educational materials, discussion, and careful attention to issues of marginalization and algorithmic justice.} }
Endnote
%0 Conference Paper %T Teaching Machine Learning in the Context of Critical Quantitative Information Literacy %A Carrie Diaz Eaton %B Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2022 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v170-eaton22a %I PMLR %P 51--56 %U https://proceedings.mlr.press/v170/eaton22a.html %V 170 %X Bates College, is a small liberal arts postsecondary institution in the northeast United States. An information literacy course, Calling Bull, serves as an introductory data science class as well as a prerequisite-free quantitative literacy class. In this context, we spend a week discussing machine learning, with an emphasis on facial recognition algorithms. The emphasis is on the general algorithmic approach, critical inquiry of the process and careful interpretation of results presented in research or decision-making. This module relies on the use of open educational materials, discussion, and careful attention to issues of marginalization and algorithmic justice.
APA
Eaton, C.D.. (2022). Teaching Machine Learning in the Context of Critical Quantitative Information Literacy. Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 170:51-56 Available from https://proceedings.mlr.press/v170/eaton22a.html.

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