My research interests reside at the intersection of human-computer interaction (HCI) and responsible artificial intelligence (AI). More concretely, I am interested in understanding laypeople’s perceptions of AI systems and their impacts on society. Through my research, I intend to examine how AI can rectify social injustices instead of perpetuating them.

I am now investigating how different ways of framing algorithmic harms can shape the public perception of decision-making algorithms. I am interested in how some of the ideologies shaping AI research, e.g., that algorithms are value neutral, can form laypeople’s understanding of algorithms deployed in high-risk domains. My primary objective is to influence the development and regulation of AI, ensuring that they are aligned with the opinions of historically marginalized groups.

During my BS and MS, I investigated people’s perceptions of moral and legal responsibility for algorithmic decision-making. My research focused on capturing whom people consider responsible when AI causes harm to embed laypeople’s opinions in normative research and policymaking to mitigate any potential conflicts between folk expectations and AI governance.

Selected Publications

  • Gabriel Lima, Nina Grgić-Hlača, Markus Langer, Yixin Zou
    Lay Perceptions of Algorithmic Discrimination in the Context of Systemic Injustice
    @ACM CHI 2025 (Honorable Mention Award)
    (Paper)
  • Gabriel Lima, Nina Grgić-Hlača, Elissa Redmiles
    Public Opinions About Copyright for AI-Generated Art: The Role of Egocentricity, Competition, and Experience
    @ACM CHI 2025
    (Paper)
  • Gabriel Lima, Nina Grgić-Hlača, Jin Keun Jeong, Meeyoung Cha
    Who Should Pay When Machines Cause Harm? Laypeople’s Expectations of Legal Damages for Machine-Caused Harm
    ACM FAccT 2023
    (Paper)
  • Gabriel Lima, Nina Grgić-Hlača, Meeyoung Cha
    Blaming Humans and Machines: What Shapes People’s Reactions to Algorithmic Harm
    ACM CHI 2023
    (Paper)
  • Gabriel Lima, Nina Grgić-Hlača, Jin Keun Jeong, Meeyoung Cha
    The Conflict Between Explainable and Accountable Decision-Making Algorithms
    ACM FAccT 2022
    (Paper)
  • Gabriel Lima, Nina Grgić-Hlača, Meeyoung Cha
    Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making
    ACM CHI 2021
    (Paper)
  • (See all publications)

Education

  • Max Planck Institute for Security and Privacy (MPI-SP)
    • Ph.D. in Computer Science, Sep 2023 - Present
  • KAIST (Korea Advanced Institute of Science and Technology)
    • M.S. in Computer Science, Mar 2021 - Feb 2023
      • Thesis: The Conflict Between Explainability and Responsibility in Algorithmic Decision-Making
      • Advisor: Meeyoung Cha
    • B.S. in Computer Science, Feb 2017 - Feb 2021
  • EPFL (École Polytechnique Fédérale de Lausanne)
    • Exchange Semester, Sep 2019 - Feb 2020