Toward A Two-Sided Fairness Framework in Search and Recommendation
Toward A Two-Sided Fairness Framework in Search and Recommendation
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DOI：
10.1145/3576840.3578332
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发表时间：
2023-03
期刊：
影响因子：
--
通讯作者：
Jiqun Liu
中科院分区：
文献类型：
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作者：
Jiqun Liu
随着人工智能(AI)辅助搜索和推荐系统在工作场所和日常生活中变得无处不在，理解和考虑公平在这类系统的设计和评估中得到了越来越多的关注。虽然有越来越多的计算研究来衡量系统的公平性以及与数据和算法相关的偏差，但超越传统机器学习(ML)管道的人类偏差的影响仍未得到充分研究。在这篇透视论文中，我们寻求开发一个双边公平框架，该框架不仅表征数据和算法偏差，而且还突出可能加剧系统偏差并导致不公平决策的认知和感知偏差。在该框架内，我们还分析了搜索和推荐事件中人与系统偏差之间的交互作用。在双边框架的基础上，我们的研究综合了应用于认知和算法去偏倚的干预和智能轻推策略，并提出了新的目标和衡量标准，用于评估系统在处理和主动缓解与数据、算法和有限理性中的偏差相关的风险方面的性能。本文独特地将关于人类偏见和制度偏见的见解整合到一个连贯的框架中，并从以人为中心的角度扩展了公平的概念。扩展的公平框架更好地反映了用户与各种形式的搜索和推荐系统互动中的挑战和机会。在信息系统设计中采用双边方法有可能提高在线去偏见的有效性，并有助于有限理性用户参与信息密集型决策。
As artificial intelligence (AI) assisted search and recommender systems have become ubiquitous in workplaces and everyday lives, understanding and accounting for fairness has gained increasing attention in the design and evaluation of such systems. While there is a growing body of computing research on measuring system fairness and biases associated with data and algorithms, the impact of human biases that go beyond traditional machine learning (ML) pipelines still remain understudied. In this Perspective Paper, we seek to develop a two-sided fairness framework that not only characterizes data and algorithmic biases, but also highlights the cognitive and perceptual biases that may exacerbate system biases and lead to unfair decisions. Within the framework, we also analyze the interactions between human and system biases in search and recommendation episodes. Built upon the two-sided framework, our research synthesizes intervention and intelligent nudging strategies applied in cognitive and algorithmic debiasing, and also proposes novel goals and measures for evaluating the performance of systems in addressing and proactively mitigating the risks associated with biases in data, algorithms, and bounded rationality. This paper uniquely integrates the insights regarding human biases and system biases into a cohesive framework and extends the concept of fairness from human-centered perspective. The extended fairness framework better reflects the challenges and opportunities in users’ interactions with search and recommender systems of varying modalities. Adopting the two-sided approach in information system design has the potential to enhancing both the effectiveness in online debiasing and the usefulness to boundedly rational users engaging in information-intensive decision-making.