# Towards Bridging the Digital Language Divide

arXiv: https://arxiv.org/abs/2307.13405 · HTML: https://mlsanity.com/paper/2307.13405/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [Examining the Inductive Bias of Neural Language Models with Artificial Languages](https://mlsanity.com/paper/2106.01044/) (2021)
- [Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages](https://mlsanity.com/paper/1903.06400/) (2019)
- [Toward More Meaningful Resources for Lower-resourced Languages](https://mlsanity.com/paper/2202.12288/) (2022)
- [Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship](https://mlsanity.com/paper/2204.05049/) (2022)
- [Language Diversity: Visible to Humans, Exploitable by Machines](https://mlsanity.com/paper/2203.04723/) (2022)
- [Reconfiguring Diversity and Inclusion for AI Ethics](https://mlsanity.com/paper/2105.02407/) (2021)
- [Representing Interlingual Meaning in Lexical Databases](https://mlsanity.com/paper/2301.09169/) (2023)

## Built on by (most cited first, 7 of 7)

- [The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?](https://mlsanity.com/paper/2601.07220/) (2026)
- [Lexical Diversity in Kinship Across Languages and Dialects](https://mlsanity.com/paper/2308.13056/) (2023)
- [Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems](https://mlsanity.com/paper/2503.04827/) (2025)
- [Pretraining Strategies using Monolingual and Parallel Data for Low-Resource Machine Translation](https://mlsanity.com/paper/2510.25116/) (2025)
- [textitVersteasch du mi? Computational and Socio-Linguistic Perspectives on GenAI, LLMs, and Non-Standard Language](https://mlsanity.com/paper/2603.28213/) (2026)
- [Disparities In Negation Understanding Across Languages In Vision-Language Models](https://mlsanity.com/paper/2604.18942/) (2026)
- [Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages](https://mlsanity.com/paper/2605.02608/) (2026)

## Abstract (arXiv)

It is a well-known fact that current AI-based language technology -- language models, machine translation systems, multilingual dictionaries and corpora -- focuses on the world's 2-3% most widely spoken languages. Recent research efforts have attempted to expand the coverage of AI technology to `under-resourced languages.' The goal of our paper is to bring attention to a phenomenon that we call linguistic bias: multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. Linguistic bias is manifested in uneven per-language performance even in the case of similar test conditions. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented, and that can even become ethically problematic as they disregard valuable aspects of diversity as well as the needs of the language communities themselves. As our attempt at building diversity-aware language resources, we present a new initiative that aims at reducing linguistic bias through both technological design and methodology, based on an eye-level collaboration with local communities.