@inproceedings{abdelgaber-etal-2025-ai,
    title = "{AI} Assistant for Socioeconomic Empowerment Using Federated Learning",
    author = "Abdelgaber, Nahed  and
      Jahan, Labiba  and
      Castellano, Nino  and
      Oltmanns, Joshua  and
      Gupta, Mehak  and
      Zhang, Jia  and
      Pednekar, Akshay  and
      Basavaraju, Ashish  and
      Velazquez, Ian  and
      Ma, Zerui",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Bizzoni, Yuri  and
      Miyagawa, So  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
    month = may,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nlp4dh-1.42/",
    pages = "490--501",
    ISBN = "979-8-89176-234-3",
    abstract = "Socioeconomic status (SES) reflects an individual{'}s standing in society, from a holistic set of factors including income, education level, and occupation. Identifying individuals in low-SES groups is crucial to ensuring they receive necessary support. However, many individuals may be hesitant to disclose their SES directly. This study introduces a federated learning-powered framework capable of verifying individuals' SES levels through the analysis of their communications described in natural language. We propose to study language usage patterns among individuals from different SES groups using clustering and topic modeling techniques. An empirical study leveraging life narrative interviews demonstrates the effectiveness of our proposed approach."
}