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[1]: Cultural bias and cultural alignment of large language models
A third approach to control cultural bias in LLMs‘ outputs, and the one we focus on in this work, is to instruct the LLM to answer like a person from another society. It is a flexible and accessible control strategy that can be used in any language, but it depends on the LLM’s capacity to accurately represent individuals and their values …
URL: https://academic.oup.com/pnasnexus/article/3/9/pgae346/7756548
[2]: Cultural Bias and cultural alignment of large Language Models
Culture fundamentally shapes people’s reasoning,behaviour,and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people’s authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five …
URL: https://arxiv.org/abs/2311.14096
[3]: Understand and Mitigate Bias in LLMs | DataCamp
The LLM has inherited society’s stereotypes due to the training data being fed into it. Another example is racial bias, in which LLMs may reflect certain ethnic groups among stereotypes, and also cultural bias of overrepresentation to fit the stereotype. The two main origins of biases in LLMs are: Data sources; Human evaluation
URL: …
The prevalence of cultural and linguistic biases in LLMs suggests that AI-generated content could inadvertently perpetuate harmful stereotypes or create culturally insensitive messaging, particularly when targeting non-English speaking markets.
The study’s findings have direct implications for global marketing campaigns. With biases more pronounced in regional languages, brands operating across multiple Asian markets face heightened risks when using AI tools for localized content. The ease with which these biases surface—frequently enough from a single prompt—indicates that even seemingly neutral marketing briefs could generate problematic content.
For the creative industry, these findings underscore the continued importance of human oversight in AI-assisted creative processes, particularly from professionals with deep understanding of local cultural contexts. As AI tools become more integrated into marketing workflows, the ability to identify and correct cultural biases will likely become a crucial skill for creative professionals working across Asian markets.
This story first appeared on Campaign asia-pacific.
Examining Cultural Bias in Large Language Models with Dr. Amy Lee
Table of Contents
As large language models (LLMs) continue too shape our digital world, concerns over cultural and linguistic biases have risen to the forefront. These biases can lead to harmful stereotypes and culturally insensitive messaging, especially in global marketing campaigns. Dr. Amy Lee, a renowned expert in AI ethics, joined us to discuss these pressing issues and explore potential solutions.
Understanding Cultural bias in AI
Senior Editor (SE): Dr. Lee, can you explain how cultural bias finds it’s way into LLMs?
Dr. Amy Lee: Absolutely. llms are trained on vast amounts of text data from society. This data inherently contains cultural assumptions and values that the model absorbs and perpetuates. This can lead to biased outputs when the model answers queries, especially in regional languages where these biases are more pronounced.
The Impact on Marketing Campaigns
SE: How do these biases affect global marketing campaigns, particularly in non-English speaking markets?
Dr. Lee: With biases more likely to surface in regional languages, brands operating across multiple Asian markets face significant risks. Even seemingly neutral marketing briefs can generate problematic content. For instance, an LLM might inadvertently reinforce stereotypes or create messaging that is insensitive to local cultures.
Mitigating Bias in LLMs
SE: What are some effective strategies to mitigate cultural biases in LLMs?
Dr. Lee: One approach is to instruct the LLM to respond as if from another society, which requires the model to accurately represent individuals and their values. However, this is challenging and depends on the LLM’s capacity. Another method is to re-evaluate and diversify the training data to ensure it reflects a more representative sample of global cultures.
The Role of Human Oversight
SE: Given these findings, how crucial is human oversight in AI-assisted creative processes?
Dr. lee: Human oversight remains vital, especially for professionals deeply grounded in local cultural contexts.As AI tools become more integrated, the ability to identify and correct biases will become a crucial skill. This ensures that AI-generated content is both accurate and respectful of diverse cultural values.
Moving Forward
SE: What final thoughts do you have for brands and businesses looking to leverage LLMs responsibly?
Dr. Lee: It’s essential to approach AI-generated content with caution and an awareness of potential biases. Regularly test and audit your content for cultural sensitivity. Ultimately, integrating ethical guidelines and human oversight will help mitigate biases and create more inclusive and respectful AI systems.
Dr. Lee’s insights provide a roadmap for brands and businesses navigating the complexities of cultural bias in LLMs. as AI continues to evolve, understanding and addressing these biases will be crucial for fostering a more inclusive digital landscape.
