# Lessons in co-creation: the inconvenient truths of inclusive sign language technology development

arXiv: https://arxiv.org/abs/2408.13171 · HTML: https://mlsanity.com/paper/2408.13171/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 53 references that are in the map.
- First posted on arXiv 23 Aug 2024.

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

- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas](https://mlsanity.com/paper/2403.02563/) (2024)

## Built on by (most cited first, 2 of 2)

- [Co-creation for Sign Language Processing and Machine Translation](https://mlsanity.com/paper/2503.01553/) (2025)
- [Telling stories, making Hanzi: AI-assisted co-creation with elderly migrants in urban China](https://mlsanity.com/paper/2507.01548/) (2025)

## Abstract (arXiv)

In the era of AI-driven language technologies, the participation of deaf communities in sign language technology development, often framed as co-creation, is increasingly emphasized. We present a reflexive case study of two Horizon 2020 projects on sign language machine translation (2021- 2023), conducted with a EUD, a European-level deaf-led NGO. Using participant observation, internal documentation, and collaborative analysis among the authors, we interrogate co-creation as both a practice and a discourse. We offer five lessons for making co-creation consequential: 1) recognise and resource deaf partners invisible labor, 2) manage expectations via accessible science communication, 3) crip co-creation by dismantling structural ableism, 4) diversify participatory methods to address co-creation fatigue and intersectionality, and 5) redistribute power through deaf leadership. We contribute an empirically grounded account of how co-creation plays out in multi-partner AI projects, and actionable implications for design that extend to participatory AI with minoritized language and disability communities.