Competing Visions of Ethical AI: A Case Study of OpenAI

2026-01-23

AI Summary

This study systematically examines how OpenAI differentially employs discourses such as “ethics,” “safety,” and “alignment” when addressing public versus academic audiences, uncovering the practical frameworks and potential biases underlying its ethical narratives. By constructing a structured textual corpus and integrating qualitative content analysis—combining inductive and deductive coding—with NLP-driven computational methods including topic modeling and data visualization, this work presents the first comparative analysis of cross-audience ethical discourse by an AI company. The findings reveal that OpenAI’s public communications heavily emphasize safety and risk narratives while significantly downplaying scholarly and advocacy-oriented ethical frameworks, thereby exposing a pattern consistent with “ethics washing.” These results provide empirical grounding for AI governance debates, and the accompanying open-sourced code supports reproducible research in this emerging domain.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Security and Privacy: Data transparency and provenance

Abstract

Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged'ethics','safety','alignment'and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communication for a general audience and communication with an academic audience, was assembled from public documentation. Analysis. Qualitative content analysis of ethical themes combined inductively derived and deductively applied codes. Quantitative analysis leveraged computational content analysis methods via NLP to model topics and quantify changes in rhetoric over time. Visualizations report aggregate results. For reproducible results, we have released our code at https://github.com/famous-blue-raincoat/AI_Ethics_Discourse. Results. Results indicate that safety and risk discourse dominate OpenAI's public communication and documentation, without applying academic and advocacy ethics frameworks or vocabularies. Conclusions. Implications for governance are presented, along with discussion of ethics-washing practices in industry.

Problem

• AI Ethics
• discourse analysis
• safety
• alignment
• ethics-washing

Innovation

• computational content analysis
• ethical AI discourse
• NLP-based topic modeling
• ethics-washing
• mixed-methods analysis