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Competing Visions of Ethical AI: A Case Study of OpenAI

Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo

arXiv:2601.16513v1cs.CY

TL;DR

Few studies have traced how OpenAI uses ethics vocabulary relative to safety and alignment over time. This paper analyzes that discourse and finds that ethics is absorbed into safety and alignment framing, jeopardizing societal and justice-oriented dimensions.

  • Problem

    Few studies have traced how a leading AI developer deploys or omits explicit ethics vocabulary relative to safety and alignment, a gap this study addresses by examining ethical framing in practice.

    Source: Introduction · p. 2

  • Method

    The study combines computational text analysis with a qualitative audit of OpenAI’s public communications to trace ethics, safety, and adjacent terminology.

    Source: Method · p. 3

  • Results

    OpenAI’s discourse absorbed ethics into safety and alignment framing, jeopardizing societal and justice-oriented dimensions while emphasizing safety, risk, and compliance over nuanced ethos or social benefits.

    Source: Discussion · pp. 10–11, Conclusions · p. 11

  • Takeaways & Limitations

    The case study concludes that AI governance and accountability must be externally imposed and reflect nuanced ethical frameworks, supported by robust and sustained multistakeholder dialogue.

    Source: Conclusions · p. 11

    The authors call for broader industry comparisons and empirical research on how public statements correspond with technical practices and compliance with evolving governance and regulation.

    Source: Future Directions · p. 11

Abstract

from arXiv · show

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.

Abstract

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Abstract

  • The passage provides incomplete journal-volume, issue, and year information rather than substantive abstract content.Source: Abstract · p. 1

Introduction

The introduction asks how OpenAI’s public discourse uses ethics, safety, alignment, and adjacent concepts over time, and what this signals about ethical framing in practice. It treats AI ethics discourse as shaping expectations and organizational practice while governance institutions and vocabularies struggle to keep pace.

Introduction

  • Broad ethical-principle frameworks offer guidance, but scholars argue their commitments often collapse when infrastructures, incentives, and usage paradigms outpace institutional capacity (Mittelstadt, 2019; Morley et al., 2021).The passage situates this challenge amid the rapid spread of general-purpose AI systems across work, politics, and everyday life.Source: Introduction · p. 2
  • AI ethics is framed as a discursive field that sets expectations, legitimizes actors, shapes policy debates, and structures organizational practice and labour roles.Language helps define responsible practice, whose expertise matters, and which risks are foregrounded or sidelined.Source: Introduction · p. 2
  • When ethics is invoked only rhetorically, it can serve as reputational cover rather than a binding constraint (Green, 2021).The introduction links language choices to the allocation of epistemic authority while governance rules are still being written.Source: Introduction · p. 2
  • Few studies trace how a leading AI developer deploys or omits explicit ethics vocabulary relative to safety and alignment; the paper examines this gap through OpenAI’s public discourse.Its research question asks how these concepts are leveraged over time and what the discourse signals about ethical framing in practice.Source: Introduction · p. 2

Background

The background contrasts principle-based AI ethics with organizational efforts to operationalize responsibility, and explains how corporate language can shape ethical commitments and public debate.

Principle-based AI ethics

  • Principle-based AI ethics emphasizes beneficence, non-maleficence, autonomy, justice, and transparency, yet principles may remain symbolic and difficult to embed in practice (Mittelstadt, 2019).These principles foreground rights, fairness, and accountability, but scholars identify a persistent gap between high-level commitments and day-to-day development, evaluation, and release processes.Source: Principle-based AI ethics · p. 2

Principles-to-Practice

Ethics-washing and Reputational Ethics

  • Ethics-washing describes ethics used as a reputational asset rather than a binding constraint, with procedural language often sidelining political or justice-oriented concerns (Green, 2021).Studies describe responsibility managed as process rather than moral constraint, responsibility displaced by powerful institutions, and ethics debates displaced outside developer communities. Hao (2025) argues unenforceable ethical statements can normalize secrecy and consolidation while masking departures from openness and collaboration.Source: Ethics-washing and Reputational Ethics · p. 3

Discourse

  • Organizational language shapes which ethical problems and solutions are legitimate, often framing responsibility as managerial process and narrowing debate toward techno-solutionism.At OpenAI, executives have framed openness and AI neutrality in particular ways, while existential safety discourse foregrounds catastrophic risks and deflects attention from nearer-term concerns such as labour displacement (Hao, 2025; Our Updated Preparedness Framework, 2025; U.S. Senate Committee on the Judiciary, Subcommittee on Privacy, Technology, and the Law., 2023).Source: Discourse · p. 3

Method

The study combines computational text analysis and qualitative auditing to trace how OpenAI framed ethics, safety, and adjacent concepts in public communications from December 2015 to July 2025. It uses a single-case corpus of OpenAI-authored and co-authored materials, analyzed through keyword tracking, topic discovery, and manual coding.

Method

  • The mixed-methods design combines large-scale computational text analysis with qualitative auditing to examine the frequency and framing of ethics, safety, and adjacent concepts.It traces OpenAI’s public communications between December 2015 and July 2025.Source: Method · p. 3

Case Selection

  • OpenAI was selected as the single case because it met four criteria: frontier model development, sustained ethics discourse, a dated public archive, and scale above the specified threshold.The single information-rich case enabled computational measurement and qualitative auditing while holding organizational context constant.Source: Case Selection · pp. 3–4

Corpus Construction and Preprocessing

  • The corpus contains 454 curated English-language documents: 424 OpenAI web articles and 30 publications, including 25 arXiv preprints and five site-hosted items.Materials came from OpenAI.com and arXiv; texts were standardized through lowercasing, punctuation and digit removal, lemmatization, and stopword removal, with paratext excluded. The corpus represents materials OpenAI elected to publish, not a complete archival record.Source: Corpus Construction and Preprocessing · p. 4

Quantitative Analysis

  • The study pairs keyword-frequency analysis with unsupervised topic modeling to track predefined concepts and discover emergent themes.It grouped terms into 75 core concepts, measured annual frequencies, and used semantic n-gram clustering with HDBSCAN to map thematic clusters over time.Source: Quantitative Analysis · p. 5

Qualitative Analysis

  • The qualitative audit examined ethics-related web articles and linked publications, coding explicit ethics terms and adjacent moral language for frequency, framing, and location.Researchers cross-checked the manually identified subset against the computational corpus, reviewed historical page snapshots and site-search results for coverage, and collaboratively resolved ambiguous cases.Source: Qualitative Analysis · pp. 5–6

Results

OpenAI’s public web discourse emphasized safety and risk, while ethics was rare and usually peripheral; ethics appeared more often in authored publications but remained limited. Across the corpora, thematic structure differed, and safety and policy shifted in function over time.

Results

  • In 2024, web articles mentioned safety 687 times and risk 386 times, while ethics peaked at seven mentions; safety and risk expanded far beyond ethics.Alignment rose to 57 mentions by 2022, governance increased in 2023–2024, and responsible reached about 50 mentions annually after 2023.Source: Results · p. 6, Figure 1 · p. 6, Figure 2 · p. 7
  • Web articles used ethics minimally and often superficially, while safety tags increased sharply in 2023 and tagging conventions changed over time.Only 7 of 52 articles tagged ‘Ethics & Safety’ used ethics in the body; the article “Planning for AGI and beyond” shifted from ‘Safety & Alignment’ to ‘Safety’.Source: Results · p. 7

OpenAI-Authored and Co-Authored Publications

  • 31 of 180 authored publications used an ethics term, but 24 of the 30 accessible items mentioned it three times or fewer, usually in peripheral contexts.Ethics references appeared in external materials, policy or guideline copy, and appeals to societal norms and values; adjacent terms such as bias and harm were rarely framed morally.Source: OpenAI-Authored and Co-Authored… · p. 8, Table 1 · p. 8
  • Ethics appeared more often in academic and collaborative publication contexts, while broader-audience website articles largely displaced ethics discourse in favor of safety.Two of the three most ethics-dense publications were co-authored with external partners.Source: OpenAI-Authored and Co-Authored… · pp. 8–9

Corpus-wide structure of OpenAI

  • Web articles clustered diverse concerns around safety, whereas publications showed more distinct thematic clusters, including policy and governance.Across both corpora, safety and risk remained prominent while ethics stayed marginal; in publications, policy prominence declined after an early 2017–2019 peak as safety, risk, and later alignment emerged.Source: Corpus-wide structure of OpenAI · pp. 9–10, Figure 3 · p. 9

Discursive Pivots: Safety and Policy

  • Safety shifted from sparse early mentions to institutionalized technical, governance, evaluation, and compliance language, then rose five-fold in 2023–2024.It acts as a unifying hub in web articles but connects specialized subfields without anchoring them in publications, functioning as a proxy for compliance, governance, and evaluation.Source: Discursive Pivots: Safety and Policy · p. 10
  • Policy mentions peaked in 2017–2018 largely through reinforcement-learning usage, then declined before governance and compliance meanings grew in 2023–2024.Later examples include ‘safety policy,’ ‘usage policy,’ and ‘policy proposals’.Source: Discursive Pivots: Safety and Policy · p. 10

Discussion

The discussion argues that OpenAI’s safety-and-alignment framing unified development but sidelined societal and justice-oriented concerns. The implications warn that omitting ethics vocabulary can reshape governance, while future work should examine industry discourse and its relation to practice and regulation.

Discussion

  • OpenAI’s safety-and-alignment framing unified development through stakeholder buy-in but sidelined societal and justice-oriented dimensions and privileged technical expertise.Safety and alignment discourse centers technical risk, compliance, and ML approaches, alienating affected communities, civil society, and social science experts.Source: Discussion · pp. 10–11

Implications

  • Ethics omission risks narrowing AI governance to compliance, risk, and market or technical priorities while redistributing epistemic authority.The authors call for transparency and accountability measures independent of organizational priorities, noting that internal framing affects design, policy, product safety, and team membership.Source: Implications · p. 11

Future Directions

  • Future research should compare ethics dialogue across firms and geopolitical contexts and test how statements correspond with technical practices and evolving regulation.Source: Future Directions · p. 11

Conclusions · About the author(s)

OpenAI’s discourse shifted toward safety, risk, and compliance rather than nuanced ethos or meaningful social benefits, and the study identifies significant ethics washing. The authors call for externally imposed governance, sustained multistakeholder dialogue, and research comparing this case with the broader industry.

Conclusions

  • The study finds significant ethics washing in OpenAI’s discourse, consistent with recent research (e.g., Hao, 2025).Source: Conclusions · p. 11
  • OpenAI’s discourse shifted over time toward safety, risk, and compliance rather than nuanced ethos or meaningful social benefits.Source: Conclusions · p. 11
  • The authors argue that AI governance and accountability must be externally imposed and reflect nuanced ethical frameworks.They link language choices to external accountability, regulatory exposure, and assurance posture.Source: Conclusions · p. 11
  • Robust, sustained multistakeholder dialogue is needed to centre human values and ethical concerns in AI development and deployment.The authors consider episodic events or partnerships insufficient.Source: Conclusions · p. 11
  • Nuanced academic ethics frameworks such as FATE or FAccT are not effectively transferred to industry practices or communications.Source: Conclusions · p. 11
  • Future research should compare this case with the broader industry and examine its impacts on society.Source: Conclusions · p. 11
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