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:
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.csrnlp-1.7.pdf