# How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP

arXiv: https://arxiv.org/abs/2104.03026 · HTML: https://mlsanity.com/paper/2104.03026/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Automatically Identifying Gender Issues in Machine Translation using Perturbations](https://mlsanity.com/paper/2004.14065/) (2020)

## Built on by (most cited first, 5 of 5)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation](https://mlsanity.com/paper/2105.13782/) (2021)
- [Experimental Standards for Deep Learning in Natural Language Processing Research](https://mlsanity.com/paper/2204.06251/) (2022)
- [Responsible AI Considerations in Text Summarization Research: A Review of Current Practices](https://mlsanity.com/paper/2311.11103/) (2023)
- [Practising responsibility: Ethics in NLP as a hands-on course](https://mlsanity.com/paper/2512.24825/) (2025)

## Abstract (arXiv)

At the Workshop on Gender Bias in NLP (GeBNLP), we'd like to encourage authors to give explicit consideration to the wider aspects of bias and its social implications. For the 2020 edition of the workshop, we therefore requested that all authors include an explicit bias statement in their work to clarify how their work relates to the social context in which NLP systems are used. The programme committee of the workshops included a number of reviewers with a background in the humanities and social sciences, in addition to NLP experts doing the bulk of the reviewing. Each paper was assigned one of those reviewers, and they were asked to pay specific attention to the provided bias statements in their reviews. This initiative was well received by the authors who submitted papers to the workshop, several of whom said they received useful suggestions and literature hints from the bias reviewers. We are therefore planning to keep this feature of the review process in future editions of the workshop.