@inproceedings{bui-etal-2026-statements,
    title = "From If-Statements to {ML} Pipelines: Revisiting Bias in Code-Generation",
    author = "Bui, Minh Duc  and
      Heilmann, Xenia  and
      Cerrato, Mattia  and
      Mager, Manuel  and
      Von Der Wense, Katharina",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.193/",
    pages = "3958--3972",
    ISBN = "979-8-89176-395-1",
    abstract = "Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming and reveal solely overt, explicitly encoded bias. We demonstrate that this approach dramatically underestimates real-world bias by examining a more realistic task: generating machine learning (ML) pipelines. Testing both code-specialized and general-instruction large language models, we find that ML pipelines exhibit substantially greater bias than simple conditionals across all conditions: standard generation, with varying prompt-based mitigation strategies, varying numbers of attributes, and different ML pipeline difficulty levels. Even attribute selection alone, the simplest pipeline difficulty, shows higher bias compared to conditionals, demonstrating that ML pipelines inherently amplify bias beyond what isolated conditionals reveal. Critically, we uncover a stark asymmetry: models maintain equivalent bias detection performance on both simple conditionals and ML pipelines, revealing that models recognize bias equally well in both contexts yet generate significantly more biased code in ML pipelines. These findings challenge simple conditionals as valid proxies for bias evaluation and suggest current benchmarks mischaracterize model safety in practical deployment contexts."
}