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Manifestations of Xenophobia in AI Systems

2022-12-15 14:58:32
Nenad Tomasev, Jonathan Leader Maynard, Iason Gabriel
arXiv_AI
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2212.07877

PDF

https://arxiv.org/pdf/2212.07877.pdf

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