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Title:More data, more problems: Strategically addressing data ethics and policy issues in LIS curricula and courses
Author(s):Jones, Kyle; Alemanne, Nicole D.; Kammer, Jenna; Shorish, Yasmeen; Barth, Grace; Taylor, Natalie G.; Hagen, Loni; Samek, Toni; Shiri, Ali; Huang, Carolin; Burgess, John; Knox, Emily
Subject(s):Information ethics
Information policy
Data ethics and policy
Library and information science education
Privacy
Abstract:Library and information science (LIS) schools are revising undergraduate and graduate curricula and individual courses to prepare students for data-centric careers, as well as to participate in a data-driven society. To meet these new challenges, programs are developing courses on, among other things, data curation, analytics, visualization, algorithm design, and artificial intelligence. While such changes reflect new workforce and society needs, it remains to be seen whether or not such efforts adequately address the very real and serious ethics and policy issues associated with related data practices (e.g., privacy, bias, fairness, and justice). The Information Ethics SIG and the Information Policy SIG have merged to present a panel on data ethics and policy issues in LIS education. In this session, two recent books on information ethics and information policy will be discussed to bring context to the panel, three papers will be presented, and the audience will have an opportunity to participate in a structured discussion. The papers will address three topics that explore the implications and concerns of living in a data-driven society: collaborative strategies for contributing to the data ethics education landscape, young adult information privacy concerns when using mobile devices, and artificial intelligence and social responsibility. The structured discussion will invite participation on issues raised by the papers, as well as implications for practice in LIS education.
Issue Date:2019-09-24
Series/Report:Information privacy
Information policy
Information ethics
Artificial intelligence
Curriculum
Education programs/schools
Data science
Genre:Conference Paper / Presentation
Type:Text
URI:http://hdl.handle.net/2142/105358
Date Available in IDEALS:2019-08-23


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