Competing Visions of Ethical AI: A Case Study of OpenAI

TL;DR

This study analyzes OpenAI's public discourse on AI ethics, revealing a focus on safety and risk that lacks engagement with broader ethical frameworks, highlighting industry ethics-washing practices.

Contribution

It provides a detailed case study of how OpenAI's ethical discourse emphasizes safety over comprehensive ethical considerations, using mixed qualitative and quantitative methods.

Findings

Safety and risk discourse dominate OpenAI's communication.

Limited use of academic and advocacy ethics frameworks.

Evidence of ethics-washing practices in industry discourse.

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.