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Classifying Ethics Codes Using Natural Language Processing

In: Responsible Organizations in the Global Context

Author

Listed:
  • Zachary Glass

    (Princeton University)

  • E. Susanna Cahn

    (Pace University)

Abstract

Business ethics scholars have varied opinions of corporate ethics codes. Many advocate them as a way to contribute to an organizational environment in which ethics will be a regular consideration in the decision-making process. Critics assert that codes of ethics are mere window dressings written to protect the company from litigation or to comply with regulations. The authors maintain that language is a key to distinguishing between these two properties and an aid to how employees and other stakeholders should view a code’s intent. However, language is often ambiguous to the reader and results of research on ethics codes are often in conflict. This chapter addresses the issue of intent by quantifying the content of ethics codes. Methodologies from natural language processing (NLP) and machine learning are applied in a novel way to classify ethics codes. Codes from companies selected from lists of “Ethical” companies are compared with codes from the Fortune 500 companies. The model’s findings indicate that ethics codes for some of these groups of companies can be distinguishable.

Suggested Citation

  • Zachary Glass & E. Susanna Cahn, 2019. "Classifying Ethics Codes Using Natural Language Processing," Springer Books, in: Annie Bartoli & Jose-Luis Guerrero & Philippe Hermel (ed.), Responsible Organizations in the Global Context, chapter 5, pages 79-96, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-11458-9_5
    DOI: 10.1007/978-3-030-11458-9_5
    as

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