Classifying Ethics Codes Using Natural Language Processing

  • Zachary Glass
  • E. Susanna Cahn


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.


Ethics codes Codes of conduct Natural language processing Machine learning Corporate social responsibility 



The authors wish to thank Adam Meyers of New York University for his invaluable advice on this project.


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Copyright information

© The Author(s) 2019

Authors and Affiliations

  • Zachary Glass
    • 1
  • E. Susanna Cahn
    • 2
  1. 1.Princeton UniversityPrincetonUSA
  2. 2.Pace UniversityNew YorkUSA

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