mlsanityData to 7 Oct 2026

Manifestations of Xenophobia in AI Systems

arXiv 2212.07877 ↗First posted 15 Dec 2022cs.CY cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 127 of its 422 references that are in the map. First posted on arXiv 15 Dec 2022.

Builds on

127 of 422 references.

References · 127

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  6. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  7. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  8. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  9. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  10. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  11. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  12. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
38 more
  1. 2018Model Cards for Model Reportingcited by 1,068
  2. 2021Ethical and social risks of harm from Language Modelscited by 957
  3. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  4. 2017Counterfactual Fairnesscited by 802
  5. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  6. 2020Transformer Interpretability Beyond Attention Visualizationcited by 620
  7. 2021Perceiver IO: A General Architecture for Structured Inputs & Outputscited by 616
  8. 2022Improving alignment of dialogue agents via targeted human judgementscited by 527
  9. 2022Socratic Models: Composing Zero-Shot Multimodal Reasoning with Languagecited by 513
  10. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  11. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  12. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  13. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  14. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  15. 2021Multi-attentional Deepfake Detectioncited by 339
  16. 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 326
  17. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 294
  18. 2021WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learningcited by 285
  19. 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
  20. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  21. 2019Massively Multilingual Neural Machine Translationcited by 246
  22. 2020HateCheck: Functional Tests for Hate Speech Detection Modelscited by 215
  23. 2019Explainable Machine Learning in Deploymentcited by 210
  24. 2022Predictability and Surprise in Large Generative Modelscited by 209
  25. 2019The Deepfake Detection Challenge (DFDC) Preview Datasetcited by 208
  26. 2019Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphologycited by 200
  27. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  28. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 170
  29. 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 169
  30. 2019Reducing Sentiment Bias in Language Models via Counterfactual Evaluationcited by 148
  31. 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 137
  32. 2020REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasetscited by 136
  33. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
  34. 2020UnQovering Stereotyping Biases via Underspecified Questionscited by 124
  35. 2021Acquisition of Chess Knowledge in AlphaZerocited by 120
  36. 2020TweepFake: about Detecting Deepfake Tweetscited by 117
  37. 2021M2TR: Multi-modal Multi-scale Transformers for Deepfake Detectioncited by 114
  38. 2021Contrastive Learning for Many-to-many Multilingual Neural Machine Translationcited by 108

50 most cited of 127 All 127 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.

  1. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 424
  2. 2023AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asiacited by 54
  3. 2025HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language modelscited by 1
  4. 2025Using LLMs as prompt modifier to avoid biases in AI image generatorscited by 1