Ethics & data

Bias in language technology, personal data in text corpora, and the copyright and consent questions raised by web-scraped training data.

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  Bias in NLP: Where It Comes From, How It Is Measured and What Helps

  A sourced introduction to bias in natural language processing: findings from word embeddings, coreference, machine translation and hate speech detection, the difference between representational and allocational harms, the limits of debiasing, and practical steps for teams.

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  GDPR and Text Data: Personal Data in NLP Corpora and Models

  How the EU General Data Protection Regulation applies to text corpora and language models: what counts as personal data in text, lawful bases and the research provisions, why anonymising text is hard, what the EDPB said about AI models in 2024, and a practical checklist.

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  Web-Scraped Corpora: Copyright, Consent and Opt-Outs in NLP

  How language models are trained on web crawls such as Common Crawl, what the EU text and data mining exceptions and the AI Act require, how robots.txt and opt-outs work, what audits of crawled corpora found, and the ethical questions consent raises.