Building AI That Keeps Users Safe Across Cultures and Crises

Imagine asking an AI chatbot for health advice and getting conflicting guidance—or turning to a chatbot in a crisis only to receive unclear instructions. Confusing or inconsistent AI isn’t just frustrating; it can put people’s health and safety at risk.

Researchers in the Computational Linguistics and Information Processing (CLIP) Lab—including Jordan Boyd-Graber, a professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS); and Vanessa Frias-Martinez, a professor in the College of Information with an appointment in UMIACS—are tackling these risks by building AI systems that people can trust.

One study, "Discrepancy Detection at the Data Level: Toward Consistent Multilingual Question Answering," examines how answers can differ across languages even when factually correct. Multilingual QA systems face the dual challenge of ensuring factual accuracy while respecting cultural relevance: a correct answer in one language may not meet expectations in another due to differences in local customs, data availability, or nuance.

To address this, the team—including researchers from UMD and the Universidad Carlos III de Madrid in Spain—created a system that proactively identifies discrepancies across languages before they appear in AI-generated answers. Dubbed MIND (Multilingual Inconsistent Notion Detection), the system aligns documents from different languages in a shared conceptual space, compares interpretations, and flags factual or culturally divergent information. For example, guidance on childbirth practices can vary by region, and MIND highlights these differences so users can trust the information.

While not fully hands-off, MIND focuses human attention mainly on flagged discrepancies, reducing the effort needed to review answers. Tested on bilingual maternal and infant health data—and other domains—MIND reliably identifies inconsistencies. By highlighting cultural differences tied to language, the system could also help reduce bias and better support underrepresented communities. To encourage broader research in culturally aware AI, the team also released a dataset of annotated bilingual questions for other researchers to build on.

The work was led by Lorena Calvo-Bartolomé, a Ph.D. student at Universidad Carlos III de Madrid, who was a visiting researcher at UMD in Fall 2024 under the supervision of Boyd-Graber. 

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