@inproceedings{lu-etal-2022-inclusion,
    title = "Inclusion in {CSR} Reports: The Lens from a Data-Driven Machine Learning Model",
    author = "Lu, Lu  and
      Gu, Jinghang  and
      Huang, Chu-Ren",
    editor = "Wan, Mingyu  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.csrnlp-1.7/",
    pages = "46--51",
    abstract = "Inclusion, as one of the foundations in the diversity, equity, and inclusion initiative, concerns the degree of being treated as an ingroup member in a workplace. Despite of its importance in a corporate{'}s ecosystem, the inclusion strategies and its performance are not adequately addressed in corporate social responsibility (CSR) and CSR reporting. This study proposes a machine learning and big data-based model to examine inclusion through the use of stereotype content in actual language use. The distribution of the stereotype content in general corpora of a given society is utilized as a baseline, with which texts about corporate texts are compared. This study not only propose a model to identify and classify inclusion in language use, but also provides insights to measure and track progress by including inclusion in CSR reports as a strategy to build an inclusive corporate team."
}