# Evaluating Bias In Dutch Word Embeddings

arXiv: https://arxiv.org/abs/2011.00244 · HTML: https://mlsanity.com/paper/2011.00244/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Gender Bias in Masked Language Models for Multiple Languages](https://mlsanity.com/paper/2205.00551/) (2022)
- [Socially Aware Bias Measurements for Hindi Language Representations](https://mlsanity.com/paper/2110.07871/) (2021)
- [Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models](https://mlsanity.com/paper/2112.07447/) (2021)
- [WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings](https://mlsanity.com/paper/2103.03598/) (2021)
- [An Empirical Study on the Characteristics of Bias upon Context Length Variation for Bangla](https://mlsanity.com/paper/2406.17375/) (2024)
- [Dutch CrowS-Pairs: Adapting a Challenge Dataset for Measuring Social Biases in Language Models for Dutch](https://mlsanity.com/paper/2507.16442/) (2025)
- [Language-Agnostic Bias Detection in Language Models with Bias Probing](https://mlsanity.com/paper/2305.13302/) (2023)
- [Data Bias According to Bipol: Men are Naturally Right and It is the Role of Women to Follow Their Lead](https://mlsanity.com/paper/2404.04838/) (2024)
- [Social Bias in Multilingual Language Models: A Survey](https://mlsanity.com/paper/2508.20201/) (2025)
- [Towards Fair and Explainable AI using a Human-Centered AI Approach](https://mlsanity.com/paper/2306.07427/) (2023)
- [Investigating Gender Bias in Turkish Language Models](https://mlsanity.com/paper/2404.11726/) (2024)
- [From Measurement to Mitigation: Exploring the Transferability of Debiasing Approaches to Gender Bias in Maltese Language Models](https://mlsanity.com/paper/2507.03142/) (2025)
- [Is She Even Relevant? When BERT Ignores Explicit Gender Cues](https://mlsanity.com/paper/2605.07622/) (2026)
- [WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ Identities](https://mlsanity.com/paper/2609.02651/) (2026)

## Abstract (arXiv)

Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world applications. This paper explores the gender bias implicit in Dutch embeddings while investigating whether English language based approaches can also be used in Dutch. We implement the Word Embeddings Association Test (WEAT), Clustering and Sentence Embeddings Association Test (SEAT) methods to quantify the gender bias in Dutch word embeddings, then we proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods and finally we evaluate the performance of the debiased embeddings in downstream tasks. The results suggest that, among others, gender bias is present in traditional and contextualized Dutch word embeddings. We highlight how techniques used to measure and reduce bias created for English can be used in Dutch embeddings by adequately translating the data and taking into account the unique characteristics of the language. Furthermore, we analyze the effect of the debiasing techniques on downstream tasks which show a negligible impact on traditional embeddings and a 2% decrease in performance in contextualized embeddings. Finally, we release the translated Dutch datasets to the public along with the traditional embeddings with mitigated bias.