Exploring User Opinions of Fairness in Recommender Systems
This work addresses fairness concerns for users and providers in recommender systems, but it is incremental as it focuses on initial exploration without implementing new methods.
The paper tackled the problem of defining fairness in recommender systems by surveying users about their opinions on fair treatment, aiming to understand discrepancies in these views to inform algorithm design.
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to trade offs between optimizing accuracy for users and fairness to providers. But what is fair in the context of recommendation--particularly when there are multiple stakeholders? In an initial exploration of this problem, we ask users what their ideas of fair treatment in recommendation might be, and why. We analyze what might cause discrepancies or changes between user's opinions towards fairness to eventually help inform the design of fairer and more transparent recommendation algorithms.