论文标题
AI系统中仇外心理的表现
Manifestations of Xenophobia in AI Systems
论文作者
论文摘要
仇外心理是边缘化，歧视和冲突的主要驱动力之一，但是许多突出的机器学习（ML）公平框架无法全面衡量或减轻产生的Xenophobic危害。在这里，我们旨在弥合这一概念差距，并有助于促进人工智能（AI）解决方案的安全和道德设计。我们通过首先识别出不同类型的仇外危害，然后在许多突出的AI应用领域中应用此框架，从而回顾了AI和Xenophobia在社交媒体和建议系统，医疗保健，移民，就业，就业，就业，以及在大型预先经过的预先经验的模型中的偏见。这些有助于为我们对未来AI系统的包容性，仇外设计设计的建议提供信息。
Xenophobia is one of the key drivers of marginalisation, discrimination, and conflict, yet many prominent machine learning (ML) fairness frameworks fail to comprehensively measure or mitigate the resulting xenophobic harms. Here we aim to bridge this conceptual gap and help facilitate safe and ethical design of artificial intelligence (AI) solutions. We ground our analysis of the impact of xenophobia by first identifying distinct types of xenophobic harms, and then applying this framework across a number of prominent AI application domains, reviewing the potential interplay between AI and xenophobia on social media and recommendation systems, healthcare, immigration, employment, as well as biases in large pre-trained models. These help inform our recommendations towards an inclusive, xenophilic design of future AI systems.
