Lena Hafner

Lena's research interest is comparative political economy, especially topics related to migration, integration and everyday political economy. She enjoys applying and developing digital humanities methodologies that help to bridge scales of analysis from individual to systemic perspectives.

Lena is co-founder of N2E, an Artificial Intelligence application that derives people's ethnicities and nationalities from their personal names. Please feel free to use the tool for your social science projects at https://www.name-to-ethnicity.com/. 

Key publications: 

Lena’s recent collaboration with researchers at the University Hospital of Innsbruck aims to identify patients at risk of limited access to healthcare through AI using a name-ethnicity classifier developed by Lena and her team:

• Palme, C., Hafner, L., et al. (2024), ‘Artificial Intelligence (AI) Reveals Ethnic Disparities in Cataract Detection and Treatment’, Ophthalmology and Therapy, doi: 10.1007/s40123-024-00945-8
• This publication uncovers bias in name-ethnicity classification AIs. To help mitigate these biases, it uses fairness-aware machine learning to present a novel AI that classifies people from different ethnicities, genders and age groups with more equal accuracy rates:
• Hafner, L., Peifer, T., and Hafner F. (2023), 'Equal Accuracy for Andrew and Abubaker – Detecting and Mitigating Bias in Name-Ethnicity Classification Algorithms’, AI & Society, doi: 10.1007/s00146-022-01619-4.
• In this publication, Lena uses social media discourse analysis to challenge the current policy dogma that immigrants' residential segregation impedes their integration: 
• Hafner, L. (2022), 'Springboard, Not Roadblock: Discourse Analysis of Facebook Groups Suggests that Ethnic Neighbourhoods in European Cities might Jump-Start Immigrants’ Integration', European Urban and Regional Studies, 29(3), pp. 383-407, doi: 10.1177/09697764211057490.