Teaching CRAAP to Robots: Artificial Intelligence, False Binaries, and Implications for Information Literacy
Author
Seeber, KevinAffiliation
University of Colorado DenverIssue Date
2018-11
Metadata
Show full item recordRights
Attribution-NonCommercial-ShareAlike 3.0 United StatesCollection Information
Proceedings from the Critical Librarianship & Pedagogy Symposium are made available by the symposium creators and the University of Arizona Libraries. Contact the CLAPS committee at https://claps2018.wordpress.com/contact/if you have questions about items in this collection.Publisher
The University of ArizonaDescription
Presentation. Critical Librarianship & Pedagogy Symposium, November 15-16, 2018, The University of Arizona.Abstract
Researchers studying artificial intelligence and semantic computing are developing algorithms capable of processing large amounts of textual data and rendering judgment on its contents. Specifically, the field of sentiment analysis is focused on creating code that applies what programmers call “common sense” to evaluate whether writing is factual or opinionated, as well as how emotional the author was. This presentation will argue that these algorithms rely on false binaries, over-simplification, and poorly-constructed checklists, similar to the approach often used when discussing information literacy with first-year college students. Instead of employing this approach, this session will argue that librarians must recognize that human interpretation lies at the core of information literacy, and that we need to embrace that complexity rather than depend on algorithmic evaluation.Type
ProceedingsLanguage
en_USThe following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States