IASEAI 2026 paper accepted! 🎉

Excited to share that our paper, “Exposing Blindspots: Cultural Bias Evaluation in Generative Image Models,” has been accepted to IASEAI 2026! 🎉

While generative image models produce striking visuals, they frequently misrepresent culture. Prior work primarily focused on text-to-image (T2I) systems, leaving image-to-image (I2I) editors largely unexamined. Our study closes this gap with a unified, reproducible evaluation that audits both T2I generation and I2I editing under a standardized protocol.

💡 Key Contributions

Our evaluation framework spans six countries (China, India, Kenya, Korea, Nigeria, U.S.), an 8-category/36-subcategory schema, and uses era-aware prompts (traditional, modern, era-agnostic).

We compare inherent T2I bias with I2I editing capability using three complementary studies:

• Multi-Loop Edit: Sequential edits testing stability and cultural fidelity under iteration
• Attribute Addition: Stepwise insertion of five distinct culture-specific attributes to quantify cumulative competence
• Cross-Country Restylization: Assessing coherent style transfer from a source country to a target country

Our platform integrates standard automatic measures (CLIPScore, DreamSim, Aesthetic Score), a culture-aware metric that combines retrieval-augmented VQA with curated knowledge, and expert human judgments from country-native reviewers.

🔍 Three Recurring Findings

1️⃣ Global-North Default: Under country-agnostic prompts, models default to Global-North, modern-leaning depictions, reducing separability between culturally distinct neighbors.

2️⃣ Metric-Human Gap in I2I: Iterative I2I editing erodes cultural fidelity, even when conventional metrics remain flat or improve.

3️⃣ Shortcut Editing: I2I models tend to apply superficial cues rather than context- and era-consistent changes.

You can find our paper at this link: https://arxiv.org/abs/2510.20042