Rethinking Algorithmic Fairness in the Context of Information Access. Issue 1 (14th October 2022)

• Record Type: Journal Article
• Authors: Udoh, Emmanuel Sebastian; Yuan, Xiaojun; Rorissa, Abebe
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 59:Issue 1(2022)
• Page Start: 815
• Page End: 817
• Publication Date: 2022-10-14
• Subjects: algorithmic bias -- Algorithmic fairness -- fair ranking -- fair recommendation system -- information access; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.736
• Languages: English
• ISSNs: 2373-9231

Abstract

ABSTRACT: The internet has revolutionized the information seeking landscape, heightening expectations that access to information will hasten the attainment of several of the United Nation (UN)'s sustainable development goals (SDGs), although digital and content divides persist and information access is demonstrably steeped in inequalities, partly due to underlying intricate, complex, and often opaque AI/ML algorithms. While equity in information access remains a long shot goal, this poster asks the fundamental question: can access to information at least be fair? And what exactly does algorithmic fairness mean in the context of information access? Drawing from themes in information search and social informatics, especially the political valence in technologies such as online decision and filtering algorithms, this poster aims to analyze current nuances of algorithmic fairness in order to identify those intricate issues that should inform and constitute any working definition and framework to study algorithmic fairness in the context of information access in an increasingly globalized world.