Rethinking Algorithmic Fairness in the Context of Information Access

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.

Studying Lived Experience and Automated Systems: The Case of Universal Credit

“…We hope this article contributes to the growing interest within the ASIS&T community around data-driven systems and algorithmic fairness and justice (Rosenbaum & Fichman, 2019;Udoh & Yuan et al, 2022;Hoffmann & Roberts et al, 2019). Theories of lived experience and qualitative longitudinal research may also be of interest to LIS (library and information science) scholars of everyday information behaviours and practices interested in incorporating reflexive and iterative research design into their studies.…”