论文标题
探索推荐系统中公平性的用户意见
Exploring User Opinions of Fairness in Recommender Systems
论文作者
论文摘要
随着这些系统在社会中变得更加普遍，人工智能的算法公平已变得越来越重要。 AI，推荐系统的一个领域，由于优化用户的准确性和对提供商的公平性之间的贸易折扣，提出了公平性的独特挑战。但是，在建议的情况下，当有多个利益相关者时，什么是公平的？在对此问题的最初探索中，我们询问用户在推荐中可能是什么公平待遇的想法，以及为什么。我们分析了可能导致用户对公平性的意见之间的差异或变化的原因，以最终帮助您设计更公平，更透明的建议算法。
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.
