# Participatory Research as a Path to Community-Informed, Gender-Fair Machine Translation

arXiv: https://arxiv.org/abs/2306.08906 · HTML: https://mlsanity.com/paper/2306.08906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://mlsanity.com/paper/2004.04498/) (2020)
- [Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT](https://mlsanity.com/paper/1802.06041/) (2018)
- [Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students](https://mlsanity.com/paper/2104.12422/) (2021)

## Built on by (most cited first, 1 of 1)

- [What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study](https://mlsanity.com/paper/2410.00545/) (2024)

## Abstract (arXiv)

Recent years have seen a strongly increased visibility of non-binary people in public discourse. Accordingly, considerations of gender-fair language go beyond a binary conception of male/female. However, language technology, especially machine translation (MT), still suffers from binary gender bias. Proposing a solution for gender-fair MT beyond the binary from a purely technological perspective might fall short to accommodate different target user groups and in the worst case might lead to misgendering. To address this challenge, we propose a method and case study building on participatory action research to include experiential experts, i.e., queer and non-binary people, translators, and MT experts, in the MT design process. The case study focuses on German, where central findings are the importance of context dependency to avoid identity invalidation and a desire for customizable MT solutions.