Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach (2407.14779v3)

Abstract: Our research investigates the impact of Generative Artificial Intelligence (GAI) models, specifically text-to-image generators (T2Is), on the representation of non-Western cultures, with a focus on Indian contexts. Despite the transformative potential of T2Is in content creation, concerns have arisen regarding biases that may lead to misrepresentations and marginalizations. Through a community-centered approach and grounded theory analysis of 5 focus groups from diverse Indian subcultures, we explore how T2I outputs to English prompts depict Indian culture and its subcultures, uncovering novel representational harms such as exoticism and cultural misappropriation. These findings highlight the urgent need for inclusive and culturally sensitive T2I systems. We propose design guidelines informed by a sociotechnical perspective, aiming to address these issues and contribute to the development of more equitable and representative GAI technologies globally. Our work also underscores the necessity of adopting a community-centered approach to comprehend the sociotechnical dynamics of these models, complementing existing work in this space while identifying and addressing the potential negative repercussions and harms that may arise when these models are deployed on a global scale.

• The paper documents severe representational harms in T2I outputs, including exoticism, cultural misappropriation, and stereotyping of Indian subcultures.
• The paper employs a community-centered approach with five focus groups and 25 participants to ground its analysis in diverse cultural perspectives.
• The paper advocates for tailored model fine-tuning, ethical design, and incorporation of cultural expertise to mitigate AI-induced harms.

Implications of Generative AI Representations of Non-Western Cultures: A Case Study on Indian Contexts

The paper "Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach" presents an incisive examination of the biases embedded within Text-to-Image (T2I) Generative AI (GAI) models, focusing particularly on their representation of Indian culture and its subcultures. The research adopts a community-centered approach, engaging directly with diverse Indian communities to document and analyze the output of these AI models.

Core Findings

Through grounded theory analysis facilitated by five focus groups comprising 25 participants from diverse Indian subcultures, the paper uncovers notable representational harms perpetuated by T2I models. These include exoticism, cultural misappropriation, and a range of well-documented representational harms such as stereotyping, erasure, and quality of service disparities.

1. Exoticism: The researchers identify a novel harm labeled exoticism, where the AI's output overamplifies certain cultural features, often at the expense of more culturally accurate details. An example can be seen in the T2I outputs consistently depicting Indian women in sarees even when prompted for more modern or Western attire. This repetitive portrayal can reinforce outdated and stereotypical imagery, contributing to a skewed perception of Indian culture.
2. Cultural Misappropriation: The paper documents how the AI models homogenize and incorrectly interpolate details from varied Indian subcultures. Participants observed outputs blending disparate cultural elements, such as combining northern saree styles with southern adornments, thus disrespecting the nuanced histories and traditions of these cultural artifacts.
3. Stereotyping and Erasure: The analysis reveals cyclic harm where certain cultural or religious subgroups within India are either stereotypically represented or entirely erased. Hinduism, particularly its northern version, is predominantly featured at the expense of India's rich religious and cultural heterogeneity. This is evident in the outputs for festivals and weddings, which ignore significant non-Hindu and non-Northern Indian practices.
4. Quality of Service and Disparagement: Clear evidence shows that the AI models offer a lower quality of service to certain socioeconomic classes, compounding disparaging stereotypes. For instance, when prompted to generate images of poor Indian families, the AI models depicted individuals with darker complexions and lesser adornments, perpetuating harmful colorist stereotypes.

Implications of Findings

From a theoretical standpoint, the research emphasizes the need to move beyond universal design paradigms that often ignore the specificity and nuance of non-Western cultural contexts. The insistence on community-centered, sociotechnical perspectives aligns well with critical HCI approaches that advocate for inclusive and participatory design processes.

Practical Implications:

1. Model Fine-Tuning: Developing more context-specific AI models can potentially mitigate the harms documented. Much like Lesan for Ethiopian languages, tailored GAI tools for specific cultural contexts can provide more precise, respectful, and accurate representations.
2. Cultural Competence and Sensitivity: Incorporating epistemic knowledge from cultural experts or community members into the training data and design processes of AI models is crucial. Models should be developed to acknowledge their limitations and include disclaimers for potentially inaccurate cultural depictions.
3. Ethical Design and Transparency: There is a clear necessity for transparency in AI development processes. AI developers should adopt ethical guidelines that ensure cultural awareness and sensitivity, with a particular focus on the evolving nature of cultures and the importance of accurate representation.

Future Directions

The paper opens pathways for further research into similar issues in other non-Western contexts, contributing to a broader understanding of global AI ethics. Subsequent research should also explore the intersectionality of cultural representation, addressing not just national but also regional and subcultural intricacies. Additionally, the paper advocates for an interdisciplinary approach combining insights from sociology, anthropology, and AI research to develop more holistic and equitable AI systems.

In conclusion, this research underscores the significant and nuanced ways in which T2I models can perpetuate representational harms, particularly toward non-Western cultures. By outlining a comprehensive set of community-informed design principles, the paper not only provides a critique but also a roadmap towards more inclusive and culturally sensitive AI model development.