# Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models

arXiv: https://arxiv.org/abs/2309.08902 · HTML: https://mlsanity.com/paper/2309.08902/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [On Measuring Social Biases in Sentence Encoders](https://mlsanity.com/paper/1903.10561/) (2019)
- [Measuring Bias in Contextualized Word Representations](https://mlsanity.com/paper/1906.07337/) (2019)
- ["I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset](https://mlsanity.com/paper/2205.09209/) (2022)
- [Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://mlsanity.com/paper/2106.14574/) (2021)
- [Nationality Bias in Text Generation](https://mlsanity.com/paper/2302.02463/) (2023)
- [BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation](https://mlsanity.com/paper/2210.07626/) (2022)
- [What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring](https://mlsanity.com/paper/1909.03567/) (2019)

## Built on by (most cited first, 30 of 32)

- [Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes](https://mlsanity.com/paper/2404.17218/) (2024)
- [A Comprehensive Evaluation of Cognitive Biases in LLMs](https://mlsanity.com/paper/2410.15413/) (2024)
- [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024)
- [Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge](https://mlsanity.com/paper/2504.07887/) (2025)
- [Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs](https://mlsanity.com/paper/2404.10160/) (2024)
- ["Global is Good, Local is Bad?": Understanding Brand Bias in LLMs](https://mlsanity.com/paper/2406.13997/) (2024)
- [Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective](https://mlsanity.com/paper/2506.05166/) (2025)
- [BiasDora: Exploring Hidden Biased Associations in Vision-Language Models](https://mlsanity.com/paper/2407.02066/) (2024)
- [AGR: Age Group fairness Reward for Bias Mitigation in LLMs](https://mlsanity.com/paper/2409.04340/) (2024)
- [Exploring Changes in Nation Perception with Nationality-Assigned Personas in LLMs](https://mlsanity.com/paper/2406.13993/) (2024)
- [Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step](https://mlsanity.com/paper/2410.03869/) (2024)
- [Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settings](https://mlsanity.com/paper/2412.06134/) (2024)
- [No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users](https://mlsanity.com/paper/2410.07589/) (2024)
- [Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMs](https://mlsanity.com/paper/2508.15831/) (2025)
- [Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs](https://mlsanity.com/paper/2601.05641/) (2026)
- [The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models](https://mlsanity.com/paper/2404.08760/) (2024)
- [Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces](https://mlsanity.com/paper/2504.13277/) (2025)
- [LLMs Do Not See Age: Assessing Demographic Bias in Automated Systematic Review Synthesis](https://mlsanity.com/paper/2511.06000/) (2025)
- [Lookism: The overlooked bias in computer vision](https://mlsanity.com/paper/2408.11448/) (2024)
- [BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Bangla](https://mlsanity.com/paper/2409.11638/) (2024)
- [Understanding Gender Bias in AI-Generated Product Descriptions](https://mlsanity.com/paper/2506.05390/) (2025)
- [Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making](https://mlsanity.com/paper/2510.06222/) (2025)
- [STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions](https://mlsanity.com/paper/2409.13843/) (2024)
- [A closer look at how large language models trust humans: patterns and biases](https://mlsanity.com/paper/2504.15801/) (2025)
- [From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models](https://mlsanity.com/paper/2506.02431/) (2025)
- [Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMs](https://mlsanity.com/paper/2410.24049/) (2024)
- [Breaking the Benchmark: Revealing LLM Bias via Minimal Contextual Augmentation](https://mlsanity.com/paper/2510.23921/) (2025)
- [Erasing 'Ugly' from the Internet: Propagation of the Beauty Myth in Text-Image Models](https://mlsanity.com/paper/2511.00749/) (2025)
- [Assessing Agentic Large Language Models in Multilingual National Bias](https://mlsanity.com/paper/2502.17945/) (2025)
- [A Customer Journey in the Land of Oz: Leveraging the Wizard of Oz Technique to Model Emotions in Customer Service Interactions](https://mlsanity.com/paper/2511.21909/) (2025)

## Abstract (arXiv)

LLMs are increasingly powerful and widely used to assist users in a variety of tasks. This use risks the introduction of LLM biases to consequential decisions such as job hiring, human performance evaluation, and criminal sentencing. Bias in NLP systems along the lines of gender and ethnicity has been widely studied, especially for specific stereotypes (e.g., Asians are good at math). In this paper, we investigate bias along less-studied but still consequential, dimensions, such as age and beauty, measuring subtler correlated decisions that LLMs make between social groups and unrelated positive and negative attributes. We ask whether LLMs hold wide-reaching biases of positive or negative sentiment for specific social groups similar to the "what is beautiful is good" bias found in people in experimental psychology. We introduce a template-generated dataset of sentence completion tasks that asks the model to select the most appropriate attribute to complete an evaluative statement about a person described as a member of a specific social group. We also reverse the completion task to select the social group based on an attribute. We report the correlations that we find for 4 cutting-edge LLMs. This dataset can be used as a benchmark to evaluate progress in more generalized biases and the templating technique can be used to expand the benchmark with minimal additional human annotation.