Conference Agenda (All times are shown in Mountain Daylight Time)

Session
Search a Great Leveler? Ensuring More Equitable Information Acquisition
Time:
Monday, 01/Nov/2021:
2:00pm - 3:30pm

Location: Salon I, Lobby Level, Marriott


External Resource:
Presentations
ID: 153 / [Single Presentation of ID 153]: 1
Panels
90 minutes
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Human Computer Interaction (HCI)
Keywords: Information equity, information acquisition, search, design

Search a Great Leveler? Ensuring More Equitable Information Acquisition

Stephann Makri1, Dana McKay6, George Buchanan2, Shanton Chang2, Dirk Lewandowski3, Andy MacFarlane1, Lynne Cole1, Sanne Vrijenhoek4, Andrés Ferraro5

1City, University of London, UK; 2Universit of Melbourne, Australia; 3Hamburg University of Applied Sciences, Germany; 4University of Amsterdam, Netherlands; 5Universitat Pompeu Fabra, Barcelona, Spain; 6RMIT, Australia

The ubiquitous search box promised to democratize knowledge access by making information universally accessible. But while many search engines cater well for certain user groups, information tasks and content types, they cater poorly for others. Poorly-served users include those with certain types of impairment (e.g. dyslexia), and weakly-supported tasks include highly exploratory goals, where it can be difficult to express information needed as a query. Furthermore, the overdominance of search functionality in many information environments has restricted support for other important forms of information acquisition, such as serendipitous information encountering and creative ‘inspiration hunting.’ Search results and recommendations can also promote certain types of content due to algorithmic bias. Rather than act as a great leveler by making information acquisition effective, efficient and enjoyable for all, search engines often unfairly favor some types of user, task or content over others. In short, search is not always equitable. This panel discussion will elucidate the inequity of search as an information acquisition paradigm from multiple perspectives and propose design principles to ensure more equitable information acquisition.