# Why AI Is WEIRD and Should Not Be This Way: Towards AI For Everyone, With Everyone, By Everyone

arXiv: https://arxiv.org/abs/2410.16315 · HTML: https://mlsanity.com/paper/2410.16315/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

## Built on by (most cited first, 10 of 10)

- [Diffusion Models Through a Global Lens: Are They Culturally Inclusive?](https://mlsanity.com/paper/2502.08914/) (2025)
- [From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs](https://mlsanity.com/paper/2505.16408/) (2025)
- [LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones?](https://mlsanity.com/paper/2503.15003/) (2025)
- [LoraxBench: A Multitask, Multilingual Benchmark Suite for 20 Indonesian Languages](https://mlsanity.com/paper/2508.12459/) (2025)
- [A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure](https://mlsanity.com/paper/2504.07531/) (2025)
- [Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarce](https://mlsanity.com/paper/2410.12691/) (2024)
- [Chumor 2.0: Towards Benchmarking Chinese Humor Understanding](https://mlsanity.com/paper/2412.17729/) (2024)
- [Conflicts in Texts: Data, Implications and Challenges](https://mlsanity.com/paper/2504.19472/) (2025)
- [NLP needs Diversity outside of 'Diversity'](https://mlsanity.com/paper/2604.14595/) (2026)
- [Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?](https://mlsanity.com/paper/2608.30866/) (2026)

## Abstract (arXiv)

This paper presents a vision for creating AI systems that are inclusive at every stage of development, from data collection to model design and evaluation. We address key limitations in the current AI pipeline and its WEIRD representation, such as lack of data diversity, biases in model performance, and narrow evaluation metrics. We also focus on the need for diverse representation among the developers of these systems, as well as incentives that are not skewed toward certain groups. We highlight opportunities to develop AI systems that are for everyone (with diverse stakeholders in mind), with everyone (inclusive of diverse data and annotators), and by everyone (designed and developed by a globally diverse workforce).