@inproceedings{ivetta-etal-2026-adaptive,
    title = "Adaptive Data Collection for {L}atin-{A}merican Community-sourced Evaluation of Stereotypes ({LACES})",
    author = "Ivetta, Guido  and
      Palombini, Pietro  and
      Martinelli, Sof{\{}{\textbackslash}'{\textbackslash}i{\}}a  and
      Gomez, Marcos J  and
      Echeveste, M Emilia  and
      Dev, Sunipa  and
      Prabhakaran, Vinodkumar  and
      Benotti, Luciana",
    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.203/",
    pages = "4177--4190",
    ISBN = "979-8-89176-395-1",
    abstract = "The evaluation of societal biases in NLP models is critically hindered by a geo-cultural gap. This leaves regions such as Latin America severely underserved, making it impossible to adequately assess or mitigate the perpetuation of harmful regional stereotypes in language technologies. This paper presents LACES, a stereotype association dataset, for 15 Latin American countries. This dataset includes 4,789 stereotype associations[The de-identified dataset can be accessed via GitHub], manually created and annotated by 83 participants. The dataset was developed through targeted community partnerships across Latin America. Additionally, in this paper, we propose a novel adaptive data collection methodology that uniquely integrates the sourcing of new stereotype entries and the validation of existing data within a single, unified workflow. This approach results in a resource with more unique stereotypes than previous static collection methods, enabling a more efficient stereotype collection. The paper further supports the quality of LACES by demonstrating reduced efficacy of debiasing methods on this dataset in comparison to existing popular stereotype benchmarks.Content Warning: This research involves the study of social biases. Consequently, the paper contains examples of discriminatory language and stereotypes that may be sensitive or upsetting to readers. These examples are included for the purpose of scientific analysis and do not reflect the views of the authors."
}