Manifestations of Xenophobia in AI Systems
This addresses the issue of marginalization and discrimination in AI for affected communities, but it is incremental as it builds on existing fairness frameworks.
The paper tackles the problem of xenophobia in AI systems by identifying distinct types of xenophobic harms and applying this framework across domains like social media, healthcare, and immigration, resulting in recommendations for inclusive, xenophilic AI design.
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.