Abstract

Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a “tool” to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges.

中文翻译：

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语义网技术和人工智能偏见：系统文献综述

摘要

由于其社会学、经济和法律影响，人工智能 (AI) 中的偏见是一个关键且及时的问题，因为有偏见的算法做出的决定可能导致对特定个人或群体的不公平待遇。已经出现了多项调查，以提供关于偏见的多学科观点或审查特定领域的偏见，例如社会科学、商业研究、刑事司法或数据挖掘。鉴于语义网 (SW) 技术支持多个 AI 系统的能力，我们回顾了语义在多大程度上可以成为解决不同算法场景中偏见的“工具”。我们对使用 SW 技术的偏见评估、表示和缓解方法进行了深入的分类和分析。我们讨论了它们在处理诸如表示特定人口统计差异或减少数据漂移、稀疏性和缺失值等问题方面的潜力。我们发现人工智能偏见的研究工作主要将语义应用于信息检索、推荐和自然语言处理应用程序，并通过多个用例论证语义可以帮助应对技术、社会学和心理挑战。