# Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas

arXiv: https://arxiv.org/abs/2403.02563 · HTML: https://mlsanity.com/paper/2403.02563/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Read and Attend: Temporal Localisation in Sign Language Videos](https://mlsanity.com/paper/2103.16481/) (2021)
- [OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages](https://mlsanity.com/paper/2110.05877/) (2021)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes](https://mlsanity.com/paper/2310.00196/) (2023)
- [What Do We Mean by "Accessibility Research"? A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019](https://mlsanity.com/paper/2101.04271/) (2021)
- [EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical Properties to Aid in Generation of Technical Gestures](https://mlsanity.com/paper/2306.01944/) (2023)
- [Gloss Alignment Using Word Embeddings](https://mlsanity.com/paper/2308.04248/) (2023)
- [Active Learning for Multilingual Fingerspelling Corpora](https://mlsanity.com/paper/2309.12443/) (2023)

## Built on by (most cited first, 19 of 19)

- [SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction](https://mlsanity.com/paper/2411.16765/) (2024)
- [SignMusketeers: An Efficient Multi-Stream Approach for Sign Language Translation at Scale](https://mlsanity.com/paper/2406.06907/) (2024)
- [Reconsidering Sentence-Level Sign Language Translation](https://mlsanity.com/paper/2406.11049/) (2024)
- [Lessons in co-creation: the inconvenient truths of inclusive sign language technology development](https://mlsanity.com/paper/2408.13171/) (2024)
- [The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition](https://mlsanity.com/paper/2409.15284/) (2024)
- [CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language](https://mlsanity.com/paper/2604.22367/) (2026)
- [ELMI: Interactive and Intelligent Sign Language Translation of Lyrics for Song Signing](https://mlsanity.com/paper/2409.09760/) (2024)
- [The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge](https://mlsanity.com/paper/2411.03568/) (2024)
- [AzSLD: Azerbaijani Sign Language Dataset for Fingerspelling, Word, and Sentence Translation with Baseline Software](https://mlsanity.com/paper/2411.12865/) (2024)
- [Training Strategies for Isolated Sign Language Recognition](https://mlsanity.com/paper/2412.11553/) (2024)
- [Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation](https://mlsanity.com/paper/2508.16076/) (2025)
- [How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse](https://mlsanity.com/paper/2510.23842/) (2025)
- ["We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools](https://mlsanity.com/paper/2410.21358/) (2024)
- [Cripping AI: Reimagining AI Through Lived Disability Experiences](https://mlsanity.com/paper/2605.02080/) (2026)
- [Phonological Perception of Sign Language Models](https://mlsanity.com/paper/2606.28667/) (2026)
- [Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks](https://mlsanity.com/paper/2609.03734/) (2026)
- [SignDino: Self-Supervised Sign Language Representation Learning via Temporal-Axis Self-Distillation](https://mlsanity.com/paper/2609.06296/) (2026)
- [Learning Sign Language Recognition under Label Noise: A Study of Noise-Robust Losses for Isolated and Continuous Settings](https://mlsanity.com/paper/2609.12885/) (2026)
- [Machine Translation for Sign Languages](https://mlsanity.com/paper/2610.00881/) (2026)

## Abstract (arXiv)

Growing research in sign language recognition, generation, and translation AI has been accompanied by calls for ethical development of such technologies. While these works are crucial to helping individual researchers do better, there is a notable lack of discussion of systemic biases or analysis of rhetoric that shape the research questions and methods in the field, especially as it remains dominated by hearing non-signing researchers. Therefore, we conduct a systematic review of 101 recent papers in sign language AI. Our analysis identifies significant biases in the current state of sign language AI research, including an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, use of annotations lacking linguistic foundations, and development of methods that build on flawed models. We take the position that the field lacks meaningful input from Deaf stakeholders, and is instead driven by what decisions are the most convenient or perceived as important to hearing researchers. We end with a call to action: the field must make space for Deaf researchers to lead the conversation in sign language AI.