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.

翻译：仇外心理是边缘化、歧视和冲突的关键驱动因素之一，然而许多著名的机器学习公平性框架未能全面衡量或缓解由此产生的仇外伤害。本文旨在弥合这一概念鸿沟，促进人工智能解决方案的安全与伦理设计。我们通过首先识别不同类型的仇外伤害来奠定对仇外心理影响的分析基础，随后将这一框架应用于多个重要的人工智能应用领域，审视人工智能与仇外在社交媒体与推荐系统、医疗保健、移民、就业以及大规模预训练模型偏见中的潜在相互作用。这些分析有助于我们提出未来人工智能系统应走向包容性、亲外性设计的建议。