Biases in Large Language Models: Origins, Inventory, and Discussion
Open accessAbstract
In this article, we introduce and discuss the pervasive issue of bias in the large language models that are currently at the core of mainstream approaches to Natural Language Processing (NLP). We first introduce data selection bias, that is, the bias caused by the choice of texts that make up a training corpus. Then, we survey the different types of social bias evidenced in the text generated by language models trained on such corpora, ranging from gender to age, from sexual orientation to ethnicity, and from religion to culture. We conclude with directions focused on measuring, reducing, and tackling the aforementioned types of bias.
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Demographic biases in large language models can impact critical decisions and have potentially severe consequences.
“These biases, which could be influenced by factors like model architecture, training data, and deployment context, can impact critical decisions and have potentially severe consequences [4].”
From Evaluating and addressing demographic disparities in medical large language models: a systematic review (2025) · cites this paper as [4]
The Introduction (page unavailable) says biases can make systems unsuitable for real-world applications and lead to sometimes catastrophic consequences; its court-system example illustrates a consequential decision. Section 2 links bias to training data and prompting, but the passages do not establish the citing sentence’s additional suggestion that model architecture influences these biases. (LLM, conf=0.82)
Experimental — accuracy not yet measuredLarge language models are trained on human-generated text that often contains biases.
“These models, however, are trained on human-generated text, which often contains biases [3–5].”
From Evaluating and addressing demographic disparities in medical large language models: a systematic review (2025) · cites this paper as [4]
LLMs can mimic biases and other deficiencies from their training data or even outright plagiarize.
“LLMs can mimic biases and other deficiencies from their training data (38–41) or even outright plagiarize (42).”
From Delving into LLM-assisted writing in biomedical publications through excess vocabulary (2025) · cites this paper as [39]
Population bias has clearly been identified as a very significant issue more generally.
“While population bias has clearly been identified as a very significant issue more generally [95]”
From The Impact of Generative AI on Creativity in Software Development: A Research Agenda (2025) · cites this paper as [95]
Randomizing the assignment of character names from a diverse pool minimizes sensitivity to tokenization problems and mitigates ethnic bias.
“We randomize the assignment of character names from a diverse pool to minimize sensitivity to tokenization problems and mitigate ethnic bias (Navigli et al., 2023; Jiang et al., 2024).”
From NoLiMa: Long-Context Evaluation Beyond Literal Matching (2025) · cites this paper as Navigli et al., 2023
Section 3.7 (Ethnicity and Race; page unavailable) discusses ethnic bias in language models and the need to avoid it, but does not address assigning character names. Searches for tokenization, diverse name pools, and randomized name assignment found no passage saying that this method reduces tokenization sensitivity or mitigates ethnic bias. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Only six studies identified biases in their results.
“Ultimately, only six studies have identified biases in their results, for example, reflecting the unequal representation of certain content or the biases inherent in human-generated text in the training data138.”
From Current applications and challenges in large language models for patient care: a systematic review (2025) · cites this paper as [138]
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Nibbling at the Hard Core of Word Sense Disambiguation
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Self-citationSelection Bias and Social Science History
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A Robust Bias Mitigation Procedure Based on the Stereotype Content ModelPreprint
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
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Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
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“Mitigating bias and improving fairness within medical M-LLMs necessitates a multifaceted approach centered on 3 pillars: data integrity, model refinement, and comprehensive evaluation [181,182].”
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Delving into LLM-assisted writing in biomedical publications through excess vocabulary
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Natural language processing in the era of large language models
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Leveraging large language models to foster equity in healthcare
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Conformity in large language models
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“(ii) We randomize the assignment of character names from a diverse pool to minimize sensitivity to tokenization problems and mitigate ethnic bias (Navigli et al., 2023; Jiang et al., 2024).”
Understanding Gender Bias in AI-Generated Product Descriptions
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Conformity in Large Language Models
Zhu, X., Zhang, C., Stafford, T., Collier, N., & Vlachos, A. (2024) · arXiv · doi:10.48550/arxiv.2410.12428
“Although large language models (LLMs) have rapidly advanced and exhibit increasingly human-like behavior (Aher et al., 2023; Kasneci et al., 2023), they are often affected by biases present in the data they are trained on (Navigli et al., 2023; Yu et al., 2023).”
- Authors
- Navigli, R., Conia, S., & Ross, B.
- Affiliations
- Sapienza University of Rome, Italy
- Sapienza University of Rome (IT)
- University of Edinburgh (GB)
- Venue
- Journal of Data and Information Quality
- Published
- 2023-06-22
- Volume
- 15
- Issue
- 2
- Pages
- 1-21
- ISSN
- 1936-1955 (ISSN-L)
- 1936-1963
- OpenAlex
- W4376643691
- Semantic Scholar
- 6d0656d9bb60a2bea50c4b894fbcc5d1e32134e7
- Cited by
- 364
- References
- 118
- Citations by year
- 2026: 107 · 2025: 164 · 2024: 81 · 2023: 12
- FWCI
- 90.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Data and Information Quality (publishedVersion, other-oa)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- European Commission (HORIZON2020, 726487)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, algorithmic bias, data selection bias, social bias, gender bias, age bias, sexual orientation bias, ethnic prejudice, religious bias, cultural bias, bias measurement, bias mitigation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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