论文标题
会话搜索中的偏见：个性化知识图的双刃剑
Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph
论文作者
论文摘要
会话AI系统用于个人设备，为用户提供高度个性化的内容。个性化知识图（PKG）是最近提出的方法之一，它将用户的信息以结构化形式存储并量身定制其喜欢的答案。但是，个性化容易扩大偏见并为回声室现象做出贡献。在本文中，我们讨论了会话搜索系统中的不同类型的偏见，重点是与PKG相关的偏见。我们回顾了文献中对偏见的现有定义：人偏见，算法偏见和两者的组合，并进一步提出了解决这些偏见的对话搜索系统的不同策略。最后，我们讨论了测量偏见和评估用户满意度的方法。
Conversational AI systems are being used in personal devices, providing users with highly personalized content. Personalized knowledge graphs (PKGs) are one of the recently proposed methods to store users' information in a structured form and tailor answers to their liking. Personalization, however, is prone to amplifying bias and contributing to the echo-chamber phenomenon. In this paper, we discuss different types of biases in conversational search systems, with the emphasis on the biases that are related to PKGs. We review existing definitions of bias in the literature: people bias, algorithm bias, and a combination of the two, and further propose different strategies for tackling these biases for conversational search systems. Finally, we discuss methods for measuring bias and evaluating user satisfaction.
