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We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods.
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
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Gender recognition or gender reductionism? The social implications of embedded gender recognition systems. In Proceedings of the 2018 chi conference on human factors in computing systems . 1–13
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On Measures of Biases and Harms in NLP
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Luwei Rose Luqiu and Fan Yang. 2018 · 2018
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Discrimination through optimization: How Facebook’s Ad delivery can lead to biased outcomes
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Nuanced metrics for measuring unintended bias with real data for text classification. In Companion proceedings of the 2019 world wide web conference . 491–500
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . 4171–4186
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Probing Toxic Content in Large Pre-Trained Language Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 4262–4274
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A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021) . 1281–1288
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Analyzing stereotypes in generative text inference tasks. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . 4052–4065
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Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . 56–67
Julia Barnett and Nicholas Diakopoulos. 2022 · 2022
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Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . 156–170
Aylin Caliskan, Pimparkar Parth Ajay, Tessa Charlesworth, Robert Wolfe, and Mahzarin R Banaji. 2022 · 2022
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An Ontology for Fairness Metrics. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . 265–275
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” Because AI is 100% right and safe”: User Attitudes and Sources of AI Authority in India. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems . 1–18
Shivani Kapania, Oliver Siy, Gabe Clapper, Azhagu Meena SP, and Nithya Sambasivan. 2022 · 2022
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All the news that’s fit to fabricate: AI-generated text as a tool of media misinformation
Sarah Kreps, R Miles McCain, and Miles Brundage. 2022 · 2022
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Studying up machine learning data: Why talk about bias when we mean power?
Milagros Miceli, Julian Posada, and Tianling Yang. 2022 · 2022
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A Study of Implicit Bias in Pretrained Language Models against People with Disabilities. In Proceedings of the 29th International Conference on Computational Linguistics . 1324–1332
Pranav Narayanan Venkit, Mukund Srinath, and Shomir Wilson. 2022 · 2022
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A survey on sentiment analysis methods, applications, and challenges
Mayur Wankhade, Annavarapu Chandra Sekhara Rao, and Chaitanya Kulkarni. 2022 · 2022
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Artificial Hallucinations in ChatGPT: Implications in Scientific Writing
Hussam Alkaissi and Samy I McFarlane. 2023 · 2023
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Survey on Sociodemographic Bias in Natural Language Processing
Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, and Rebecca J Passonneau. 2023 · 2023
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The Batch: ChatGPT Mania!, Crypto Fiasco Defunds AI Safety, Alexa Tells Bedtime Stories
Andrew Ng. 2023 · 2023
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Nationality Bias in Text Generation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics . 116–122
Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting-Hao Huang, and Shomir Wilson. 2023 · 2023
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