Research
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/.
Publications
Lena’s recent 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 shows how digital humanities can uproot conventional social science wisdom. By using social media discourse analysis, she challenges 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.
