Language-Tech Cognitive Frameworks
Examining how language shapes technology development and use across cultures and contexts, with a focus on ensuring linguistic diversity and inclusion in AI systems.
Research Overview
The Language-Tech Cognitive Frameworks research area examines the complex interplay between language, culture, and technology. As AI systems increasingly mediate human communication and information access, understanding how language shapes these interactions becomes critical for ensuring equitable and culturally appropriate technology development.
Our research in this area spans theoretical frameworks, empirical studies, and practical applications, with a particular focus on:
- Cross-Cultural NLP: Investigating how natural language processing systems perform across different languages, dialects, and cultural contexts, with attention to bias, accuracy, and representation.
- Language Justice: Developing frameworks for ensuring linguistic diversity and inclusion in AI systems, with a focus on low-resource languages and marginalized linguistic communities.
- Interface Design: Exploring how language shapes user interfaces and human-computer interaction, with attention to cultural appropriateness, accessibility, and user agency.
- Translation Ethics: Examining the ethical implications of machine translation and cross-linguistic AI systems, including issues of accuracy, context, and cultural nuance.
Current Projects
Multilingual Bias Observatory
A systematic study of bias patterns across multiple languages in large language models, with a focus on developing standardized measurement tools and mitigation strategies.
Low-Resource Language Inclusion Initiative
A collaborative project working with linguistic communities to develop resources, datasets, and models for languages traditionally underrepresented in AI systems.
Cultural Context in Machine Translation
Research examining how cultural context affects machine translation quality and appropriateness, with a focus on developing more culturally aware translation systems.
Linguistic Interface Design Guidelines
Development of evidence-based guidelines for creating technology interfaces that respect linguistic diversity and cultural context.
Research Methodology
Our research in this area employs a mixed-methods approach, combining:
- Computational Analysis: Quantitative evaluation of language models, translation systems, and other NLP technologies across diverse linguistic contexts.
- Participatory Research: Collaborative work with linguistic communities to understand needs, challenges, and opportunities.
- Ethnographic Studies: In-depth qualitative research on how language technologies are used and experienced in different cultural contexts.
- Design Research: Experimental development and testing of alternative interface designs and interaction patterns.
Featured Publication
Linguistic Bias in Large Language Models: Measurement and Mitigation
This comprehensive report provides detailed analysis of how bias manifests differently across languages in large language models, along with practical recommendations for measuring and mitigating these biases.
Read the report