research

My research interests lie in AI and algorithmic fairness, and natural language processing (NLP). The goal of my work, anchored in intersectionality and a multicultural interdisciplinary perspective, is to

  • understand the social impact of generative AI on users and society (such as understanding social biases and representation)
  • develop robust algorithms, frameworks, and evaluations for addressing and understanding the social impact of generative AI in varying contexts

2025

  1. More of the Same: Persistent Representational Harms Under Increased Representation
    Jennifer Mickel, Maria De-Arteaga, Leqi Liu, and 1 more author
    2025

2024

  1. Evaluating the Social Impact of Generative AI Systems in Systems and Society
    Irene Solaiman, Zeerak Talat, William Agnew, and 28 more authors
    2024
  2. Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter and What They Represent
    Jennifer Mickel
    In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024
  3. Intersectional Insights for Robust Models: Introducing FOG 😶‍🌫️ for Improving Worst Case Performance Without Group Information
    Jennifer Mickel
    Turing Scholars Honors Thesis, 2024

2023

  1. The Importance of Multi-Dimensional Intersectionality in Algorithmic Fairness and AI Model Development
    Jennifer Mickel
    Polymathic Scholars Honors Thesis, 2023