# Large Language Models are Biased Because They Are Large Language Models

arXiv: https://arxiv.org/abs/2406.13138 · HTML: https://mlsanity.com/paper/2406.13138/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020)

## Built on by (most cited first, 26 of 26)

- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)](https://mlsanity.com/paper/2407.12858/) (2024)
- ['Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs](https://mlsanity.com/paper/2409.13484/) (2024)
- [Sherkala-Chat: Building a State-of-the-Art LLM for Kazakh in a Moderately Resourced Setting](https://mlsanity.com/paper/2503.01493/) (2025)
- [Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room](https://mlsanity.com/paper/2504.16148/) (2025)
- [AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models](https://mlsanity.com/paper/2509.02133/) (2025)
- [ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents](https://mlsanity.com/paper/2510.16381/) (2025)
- [Problems With Large Language Models for Learner Modelling: Why LLMs Alone Fall Short for Responsible Tutoring in K--12 Education](https://mlsanity.com/paper/2512.23036/) (2025)
- [Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling](https://mlsanity.com/paper/2604.08263/) (2026)
- [A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains](https://mlsanity.com/paper/2511.01840/) (2025)
- [What AI Speaks for Your Community: Polling AI Agents for Public Opinion on Data Center Projects](https://mlsanity.com/paper/2511.22037/) (2025)
- [Language models as tools for investigating the distinction between possible and impossible natural languages](https://mlsanity.com/paper/2512.09394/) (2025)
- [Large language models have learned to use language](https://mlsanity.com/paper/2512.12447/) (2025)
- [Large language models are not about natural language](https://mlsanity.com/paper/2512.13441/) (2025)
- [Linguists should learn to love speech-based deep learning models](https://mlsanity.com/paper/2512.14506/) (2025)
- [BiasLab: A Multilingual Dual-Framing Framework for LLM Bias Measurement, Applied to Workplace and HR Contexts](https://mlsanity.com/paper/2601.06861/) (2026)
- [Are Language Models Models?](https://mlsanity.com/paper/2601.10421/) (2026)
- [Adaptive Prompt Elicitation for Text-to-Image Generation](https://mlsanity.com/paper/2602.04713/) (2026)
- [Simulating Meaning, Nevermore! Introducing ICR: A Semiotic-Hermeneutic Metric for Evaluating Meaning in LLM Text Summaries](https://mlsanity.com/paper/2603.04413/) (2026)
- [The Third Ambition: Artificial Intelligence and the Science of Human Behavior](https://mlsanity.com/paper/2603.07329/) (2026)
- [Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities](https://mlsanity.com/paper/2604.09466/) (2026)
- [You Can't Fight in Here! This is BBS!](https://mlsanity.com/paper/2604.09501/) (2026)
- [Auditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language models](https://mlsanity.com/paper/2605.01451/) (2026)
- [Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear](https://mlsanity.com/paper/2605.10061/) (2026)
- [Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection](https://mlsanity.com/paper/2608.03627/) (2026)
- [Language, Language Models, and What We're Talking About](https://mlsanity.com/paper/2609.03577/) (2026)

## Abstract (arXiv)

This position paper's primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models. I do this by seeking to convince the reader that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.