mlsanityData to 14 Oct 2026

Toxic Bias: Perspective API Misreads German as More Toxic

arXiv 2312.12651 ↗First posted 19 Dec 2023cs.SIcited by 21

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Abstract (arXiv)

Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an important bias in Perspective API concerning German language text. Through an in-depth examination of several datasets, we uncover intrinsic language biases within the multilingual model of Perspective API. We find that the toxicity assessment of German content produces significantly higher toxicity levels than other languages. This finding is robust across various translations, topics, and data sources, and has significant consequences for both research and moderation strategies that rely on Perspective API. For instance, we show that, on average, four times more tweets and users would be moderated when using the German language compared to their English translation. Our findings point to broader risks associated with the widespread use of proprietary APIs within the computational social sciences.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Dec 2023.

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Citing papers per year: 2024: 6; 2025: 10; 2026: 5.

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  1. 2024Aya Model: An Instruction Finetuned Open-Access Multilingual Language Modelcited by 295
  2. 2024PolygloToxicityPrompts: Multilingual Evaluation of Neural Toxic Degeneration in Large Language Modelscited by 20
  3. 2024From One to Many: Expanding the Scope of Toxicity Mitigation in Language Modelscited by 18
  4. 2024HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twittercited by 13
  5. 2025Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variationscited by 13
  6. 2025MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attackscited by 10
  7. 2024Testing the Limits of Jailbreaking Defenses with the Purple Problemcited by 8
  8. 2025Evaluating open-source Large Language Models for automated fact-checkingcited by 6
  9. 2025Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifierscited by 5
  10. 2025Aligned Probing: Relating Toxic Behavior and Model Internalscited by 5
  11. 2025Digital Guardians: Can GPT-4, Perspective API, and Moderation API reliably detect hate speech in reader comments of German online newspapers?cited by 3
  12. 2024A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communitiescited by 2
9 more
  1. 2025Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speechcited by 2
  2. 2025Evaluating Chinese Large Language Models: The Influence of Persona Assignment on Stereotypes and Safeguardscited by 2
  3. 2026Bye Bye Perspective API: Lessons for Building and Governing Measurement Infrastructurecited by 1
  4. 2025Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classificationcited by 0
  5. 2025BIPOLAR: Polarization-based granular framework for LLM bias evaluationcited by 0
  6. 2026Characterising Toxicity in Generative Large Language Modelscited by 0
  7. 2026Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflictscited by 0
  8. 2026BenCSSmark: Making the Social Sciences Count in LLM Researchcited by 0
  9. 2026Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentimentcited by 0