Understanding Intrinsic Socioeconomic Biases in Large Language Models
- URL: http://arxiv.org/abs/2405.18662v1
- Date: Tue, 28 May 2024 23:54:44 GMT
- Title: Understanding Intrinsic Socioeconomic Biases in Large Language Models
- Authors: Mina Arzaghi, Florian Carichon, Golnoosh Farnadi,
- Abstract summary: We introduce a novel dataset of one million English sentences to quantify socioeconomic biases.
Our findings reveal pervasive socioeconomic biases in both established models like GPT-2 and state-of-the-art models like Llama 2 and Falcon.
- Score: 4.276697874428501
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applications, where inherent biases can lead to discriminatory outcomes. In this paper, we examine the nuanced relationship between demographic attributes and socioeconomic biases in LLMs, a crucial yet understudied area of fairness in LLMs. We introduce a novel dataset of one million English sentences to systematically quantify socioeconomic biases across various demographic groups. Our findings reveal pervasive socioeconomic biases in both established models such as GPT-2 and state-of-the-art models like Llama 2 and Falcon. We demonstrate that these biases are significantly amplified when considering intersectionality, with LLMs exhibiting a remarkable capacity to extract multiple demographic attributes from names and then correlate them with specific socioeconomic biases. This research highlights the urgent necessity for proactive and robust bias mitigation techniques to safeguard against discriminatory outcomes when deploying these powerful models in critical real-world applications.
Related papers
- Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models [10.565316815513235]
Large language models (LLMs) may still exhibit implicit biases when simulating human behavior.
We show that state-of-the-art LLMs exhibit significant sociodemographic disparities in nearly all simulations.
When comparing our findings to real-world disparities reported in empirical studies, we find that the biases we uncovered are directionally aligned but markedly amplified.
arXiv Detail & Related papers (2025-01-29T05:21:31Z) - Fairness in LLM-Generated Surveys [0.5720786928479238]
Large Language Models (LLMs) excel in text generation and understanding, especially simulating socio-political and economic patterns.
This study examines how LLMs perform across diverse populations by analyzing public surveys from Chile and the United States.
Political identity and race significantly influence prediction accuracy, while in Chile, gender, education, and religious affiliation play more pronounced roles.
arXiv Detail & Related papers (2025-01-25T23:42:20Z) - Gender Bias of LLM in Economics: An Existentialism Perspective [1.024113475677323]
This paper investigates gender bias in large language models (LLMs)
LLMs reinforce gender stereotypes even without explicit gender markers.
We argue that bias in LLMs is not an unintended flaw but a systematic result of their rational processing.
arXiv Detail & Related papers (2024-10-14T01:42:01Z) - Social Debiasing for Fair Multi-modal LLMs [55.8071045346024]
Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities.
However, these models often inherit severe social biases from their training datasets, leading to unfair predictions based on attributes like race and gender.
This paper addresses the issue of social biases in MLLMs by i) Introducing a comprehensive Counterfactual dataset with Multiple Social Concepts (CMSC) and ii) Proposing an Anti-Stereotype Debiasing strategy (ASD)
arXiv Detail & Related papers (2024-08-13T02:08:32Z) - VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model [72.13121434085116]
We introduce VLBiasBench, a benchmark to evaluate biases in Large Vision-Language Models (LVLMs)
VLBiasBench features a dataset that covers nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status, as well as two intersectional bias categories: race x gender and race x social economic status.
We conduct extensive evaluations on 15 open-source models as well as two advanced closed-source models, yielding new insights into the biases present in these models.
arXiv Detail & Related papers (2024-06-20T10:56:59Z) - Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective [66.34066553400108]
We conduct a rigorous evaluation of large language models' implicit bias towards certain demographics.
Inspired by psychometric principles, we propose three attack approaches, i.e., Disguise, Deception, and Teaching.
Our methods can elicit LLMs' inner bias more effectively than competitive baselines.
arXiv Detail & Related papers (2024-06-20T06:42:08Z) - Exploring Value Biases: How LLMs Deviate Towards the Ideal [57.99044181599786]
Large-Language-Models (LLMs) are deployed in a wide range of applications, and their response has an increasing social impact.
We show that value bias is strong in LLMs across different categories, similar to the results found in human studies.
arXiv Detail & Related papers (2024-02-16T18:28:43Z) - Survey of Social Bias in Vision-Language Models [65.44579542312489]
Survey aims to provide researchers with a high-level insight into the similarities and differences of social bias studies in pre-trained models across NLP, CV, and VL.
The findings and recommendations presented here can benefit the ML community, fostering the development of fairer and non-biased AI models.
arXiv Detail & Related papers (2023-09-24T15:34:56Z) - Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models [0.0]
This paper investigates bias along less-studied but still consequential, dimensions, such as age and beauty.
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.
arXiv Detail & Related papers (2023-09-16T07:07:04Z) - Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs)
We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing.
We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
arXiv Detail & Related papers (2023-09-02T00:32:55Z) - The Unequal Opportunities of Large Language Models: Revealing
Demographic Bias through Job Recommendations [5.898806397015801]
We propose a simple method for analyzing and comparing demographic bias in Large Language Models (LLMs)
We demonstrate the effectiveness of our method by measuring intersectional biases within ChatGPT and LLaMA.
We identify distinct biases in both models toward various demographic identities, such as both models consistently suggesting low-paying jobs for Mexican workers.
arXiv Detail & Related papers (2023-08-03T21:12:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.