By Safia Mizra
Imagine you are interacting with someone who does not understand your culture, language, and social background. Do you expect an appropriate response? There is a high possibility of misunderstanding, misinterpretation, and biased outcomes. Artificial intelligence has become a significant aspect of daily communication, education, and language-related activities. However, AI is trained by Western-centric ideology, which may reinforce existing stereotypes. As AI is integrated into the daily lives of people, it is expected to serve diverse cultural, linguistic, and social backgrounds to avoid the risk of culturally insensitive outcomes. In a world characterized by cultural diversity, building culturally inclusive AI is not merely a technological challenge—it is an ethical and social responsibility. Cultural representation in AI can improve fairness, enhance users’ experiences, and reduce biased outcomes, ensuring that technological advancement benefits individuals across all cultural backgrounds.
Understanding the Relationship Between AI and Culture
Culture influences nearly every aspect of human behavior. It shapes language, style, communication, traditions, beliefs, and the way of expressing and interpreting information. There are various meanings of gestures and language expressions across cultures. People of different cultural backgrounds may approach problems differently and have different expectations regarding technology. The study by UNESCO (2023) estimates that AI tools represent less than 1% of the world’s languages. However, if the AI does not incorporate an understanding of languages and their culture, the accuracy of its responses will be compromised. AI technologies that fail to recognize such cultures difference creating experiences of exclusion that scholars call ethical imperialism.
Why Cultural Diversity Matters in AI Development
The global adoption of AI means it has been used by people of diverse backgrounds. Its users are spread across the world; there is significant variation in their cultures. Many studies have demonstrated that AI models are designed on Western ideologies, which may prioritize their beliefs and values and reinforce historical stereotypes towards other cultures. Liu (2024) stated that even outside the Western cultural context, when it is applied to other languages or cultures, the LLMs default to Western cultural assumptions. Therefore, it is paramount that these models should have a nuance understanding of culture to provide better support, fair responses, and reduce misinterpretations.
1. Preventing Global Inequalities
Western-centric cultural data trains AI models. Its responses sometimes reflect cultural hierarchies, as these models perpetuate the cultural biases embedded in their training data. Generative systems often replicate stereotypes and steer users toward prevailing linguistic and cultural norms (Azgın & Kıralp, 2024). Therefore, these systems sometimes misrepresent the values and lived experiences of non-Western societies (Youvan, 2024). By expanding culturally trained data, it can better respect and preserve local heritage and languages.
2. Adapting Different Value Systems
Different societies have distinct ethical values in unique ways. The Moral Machine experiment was a worldwide study that collected millions of responses on ethical dilemmas involving AI. People were asked how a self-driving car should act in an unavoidable accident. Some individuals, particularly those from collectivist cultures, preferred decisions that benefit the majority of people, while others from individualistic cultures gave greater importance to individual rights and protection. These differences highlight why cultural perspectives are important when designing AI systems (Awad et al., 2018). This demonstrates that ethical decisions are also influenced by culture; understanding cultures is crucial for AI models to make accurate decisions.
3. Provide Accurate and Relevant Responses
Limited diversity in AI development, from engineering teams to data training, makes it less interactive in real-world situations. The creators of AI and the data they utilize to train their models impact the results. If AI developers and their datasets do not intentionally embrace cultural pluralism, they may inadvertently perpetuate ethical imperialism and digital colonization.
4. Promoting Culturally Inclusive and Ethical AI Development
The United Nations and organizations such as UNESCO have consistently emphasized that failing to address bias and cultural homogenization in AI can undermine cultural rights and diversity. To uphold cultural pluralism, global ethical frameworks advocate developing AI systems adaptable to local norms, values, and philosophical perspectives. Furthermore, including human supervision and collaborative design practices ensure that AI technologies empower local communities instead of imposing a single dominant worldview.
To build inclusive AI ecosystems, developers and organizations must prioritize expanding data diversity, conduct ongoing fairness audits, and involve underserved voices from the very beginning of the AI development lifecycle.
Strategies for Developing Inclusivity in AI
There are several strategies to create culturally more inclusive AI:
- Ethical pluralism encompasses global ethical frameworks.
- Involve multicultural teams throughout the development process.
- Culturally grounded governance and language preservation.
- Implement cultural auditing and accountability.
- Collaborate with cultural and linguistic experts.
- Design AI systems that adapt to local contexts and user needs.
- Encourage international cooperation in AI research and policymaking.
These approaches help ensure that AI technologies remain responsive to the diversity of their users while promoting ethical and inclusive innovation.
Case Studies of Successful Culturally Aware AI Systems
Duolingo
This language learning app is used by people of diverse backgrounds. It utilizes AI to create lessons and exercises for users based on their cultural context. It includes culturally relevant language expressions and idioms that make language learning more engaging for its users.
IBM Watson for Oncology
It analyzes patient data and considers cultural factors when prescribing treatments. This approach ensures that recommendations align with patients’ values and beliefs, improving acceptance and adherence to treatment plans.
Culturally Adapted Chatbots
Many institutes have successfully implemented chatbots that integrate cultural understanding. For instance, the CATE-AI chatbot is used in the educational system to teach learners how to connect cultural values with global technology.
These case studies demonstrate the potential of AI to operate in diverse cultural contexts, fostering inclusivity and user satisfaction.
Main benefits for users
- More inclusion, because diverse cultural representation helps serve minority languages and underrepresented communities.
- AI can understand local languages, idioms, and cultural context, which improves its accuracy.
- Increase users’ trust because they feel valued when the system reflects their identity, values, and communication style.
- Fewer harmful errors, since culturally aware models are less likely to misinterpret sensitive phrases or reinforce stereotypes.
Conclusion
In a nutshell, today’s world heavily relies on technologies, especially on AI. These tools are developed by humans, so there is a high chance that they may perpetuate existing stereotypes. To avoid this, it is essential to incorporate cultural understanding into AI systems to reduce biased and culturally insensitive responses for users from diverse backgrounds. Therefore, ensuring that the development of AI is equitable and inclusive of diverse cultures constitutes both an ethical imperative and a necessary condition for advancing social justice in technological innovation.
Reference
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A.,& Rahwan, I. (2018). The moral machine experiment. Nature, 563(7729), 59-64.
Azgın, B., & Kıralp, S. Z. (2024). Generative AI and linguistic homogenization. Journal of Digital Cultures, 15(2), 201–218.
Liu, Z. (2023). Cultural bias in large language models: A comprehensive analysis and mitigation strategies. Journal of Transcultural Communication, 3(2), 224-244.
Youvan, D. C. (2024). Ethical pluralism in AI: Challenging the monolithic values of red teams. Retrieved fromhttps://www.researchgate.net/publication/384014537_Ethical_Pluralism_in_AI_Challenging_the_Monolithic_Values_of_Red_Teams
