학술논문

Matching Consumer Fairness Objectives & Strategies for RecSys
Document Type
Working Paper
Source
Subject
Computer Science - Information Retrieval
Computer Science - Human-Computer Interaction
Language
Abstract
The last several years have brought a growing body of work on ensuring that recommender systems are in some sense consumer-fair -- that is, they provide comparable quality of service, accuracy of representation, and other effects to their users. However, there are many different strategies to make systems more fair and a range of intervention points. In this position paper, we build on ongoing work to highlight the need for researchers and practitioners to attend to the details of their application, users, and the fairness objective they aim to achieve, and adopt interventions that are appropriate to the situation. We argue that consumer fairness should be a creative endeavor flowing from the particularities of the specific problem to be solved.
Comment: Paper presented at FAccTRec 2022