# On The Conceptualization and Societal Impact of Cross-Cultural Bias

arXiv: https://arxiv.org/abs/2512.21809 · HTML: https://mlsanity.com/paper/2512.21809/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## 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)
- [Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://mlsanity.com/paper/2412.03304/) (2024)
- [On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena](https://mlsanity.com/paper/2501.04662/) (2025)
- [See It from My Perspective: How Language Affects Cultural Bias in Image Understanding](https://mlsanity.com/paper/2406.11665/) (2024)
- [Arabic Dataset for LLM Safeguard Evaluation](https://mlsanity.com/paper/2410.17040/) (2024)
- [GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge Benchmarking](https://mlsanity.com/paper/2502.13766/) (2025)
- [CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis](https://mlsanity.com/paper/2505.19484/) (2025)
- [Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models](https://mlsanity.com/paper/2502.14906/) (2025)
- [Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors](https://mlsanity.com/paper/2506.06987/) (2025)
- [Attributing Culture-Conditioned Generations to Pretraining Corpora](https://mlsanity.com/paper/2412.20760/) (2024)

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

- [Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook](https://mlsanity.com/paper/2604.06210/) (2026)

## Abstract (arXiv)

Research has shown that while large language models (LLMs) can generate their responses based on cultural context, they are not perfect and tend to generalize across cultures. However, when evaluating the cultural bias of a language technology on any dataset, researchers may choose not to engage with stakeholders actually using that technology in real life, which evades the very fundamental problem they set out to address. Inspired by the work done by arXiv:2005.14050v2, I set out to analyse recent literature about identifying and evaluating cultural bias in Natural Language Processing (NLP). I picked out 20 papers published in 2025 about cultural bias and came up with a set of observations to allow NLP researchers in the future to conceptualize bias concretely and evaluate its harms effectively. My aim is to advocate for a robust assessment of the societal impact of language technologies exhibiting cross-cultural bias.