Counteracting Bias and Increasing Fairness in Search and Recommender Systems


引用 23|浏览40
ABSTRACT Search and recommender systems have unprecedented influence on how and what information people access. These gateways to information on the one hand create an easy and universal access to online information, and on the other hand create biases that have shown to cause knowledge disparity and ill-decisions for information seekers. Most of the algorithms for indexing, retrieval, ranking, and recommendation are heavily driven by the underlying data that itself is biased. In addition, ordering of the search and recommendation results create position bias and exposure bias due to their considerable focus on relevance and user satisfaction. These and other forms of biases that are implicitly and some times explicitly woven in search and recommender systems are becoming increasing threats to information seeking and sense-making processes. In this tutorial, we will introduce the issues of biases in search and recommendation and show how we could think about and create systems that are fairer, with increasing diversity and transparency. Specifically, the tutorial will present several fundamental concepts such as relevance, novelty, diversity, bias, and fairness using socio-technical terminologies taken from various communities, and dive deeper into metrics and frameworks that allow us to understand, extract, and materialize them. The tutorial will cover some of the most recent works in this area and show how this interdisciplinary research has opened up new challenges and opportunities for communities such as RecSys.
bias and fairness, diversity and novelty, evaluation, social effects
AI 理解论文
Chat Paper