Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model

Lu Lu, Jinghang Gu, Chu-Ren Huang


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.
Anthology ID:
2022.csrnlp-1.7
Volume:
Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
CSRNLP
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
46–51
Language:
URL:
https://aclanthology.org/2022.csrnlp-1.7
DOI:
Bibkey:
Cite (ACL):
Lu Lu, Jinghang Gu, and Chu-Ren Huang. 2022. Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model. In Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference, pages 46–51, Marseille, France. European Language Resources Association.
Cite (Informal):
Inclusion in CSR Reports: The Lens from a Data-Driven Machine Learning Model (Lu et al., CSRNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/remove-xml-comments/2022.csrnlp-1.7.pdf