AI ethics varies widely across nations and sectors, challenging global alignment

Global discussions on artificial intelligence ethics are far from unified, exposing deep institutional and geopolitical divisions in how principles such as fairness, privacy, and accountability are defined and applied, says a new analysis published in AI & Society.

The study, titled "Examining Trends in AI Ethics Across Countries and Institutions via Quantitative Discourse Analysis," systematically dissects ten leading AI ethics frameworks, from government agencies, technology companies, academia, and the military, issued between 2018 and 2021.

The findings highlight how each institution interprets ethical values through its own ideological lens, often reshaping them to align with national, commercial, or security interests. The study concludes that despite the global surge in AI governance initiatives, there is no consistent ethical consensus guiding their development.

The myth of universal AI ethics

The research assesses whether the world's most influential AI ethics frameworks share a universal understanding of core moral principles, or whether their meanings shift based on institutional context. Using a dataset of 2,351 coded segments across 14 ethical categories, the analysis uncovered striking disparities in emphasis, terminology, and intent.

According to the findings, privacy, fairness, and accountability appear frequently across nearly all frameworks, but their interpretations vary dramatically. European Union documents emphasize privacy as a fundamental right tied to democratic governance, while U.S. military and security documents frame it as a matter of operational control and data management. In contrast, Israeli frameworks interpret privacy collectively, positioning it within the context of national resilience and security.

Similarly, the notion of fairness differs across sectors. Industry codes, such as those from major technology companies, treat fairness primarily as a technical challenge, reducing algorithmic bias through optimization and data quality. Academic and civil society frameworks, on the other hand, frame fairness as a social justice issue linked to equity and discrimination.

The study also found that terms like governability and traceability appear almost exclusively in military and defense-oriented documents, revealing priorities around maintaining oversight and command control of autonomous systems. Conversely, equity and inclusivity feature prominently in academic and policy-oriented literature but are largely absent in private sector frameworks. These discrepancies underscore that institutions are not simply applying shared principles differently, they are redefining what those principles mean.

The author's quantitative approach confirms that ethical concepts do not exist in isolation; they are shaped by institutional missions. For example, autonomy in academia often refers to human freedom and moral agency, whereas in defense or industry contexts, it denotes the independent decision-making capacity of AI systems. This inversion of meaning illustrates how technology's moral vocabulary is constantly re-engineered to fit strategic objectives.

Institutions, ideology, and the politics of AI ethics

Next up, the study addresses how institutional agendas influence the framing of AI ethics. The results reveal clear ideological patterns that divide academic, industrial, military, and governmental approaches.

Academic frameworks prioritize human agency, fairness, and equity, drawing on philosophical traditions that connect technology to broader questions of democracy, rights, and justice. They emphasize normative reflection and critique, often warning against unregulated AI expansion.

Industry frameworks, in contrast, revolve around technical feasibility and market trust. Corporate documents focus heavily on explainability, safety, and accountability, principles that align with maintaining consumer confidence and reducing legal risk. Ethics, in this context, becomes an engineering goal rather than a philosophical inquiry.

Military and defense frameworks exhibit the most distinctive ethical vocabulary, dominated by terms like security, control, and traceability. These emphasize the need for human oversight, chain of command, and reliability in autonomous systems, revealing a clear priority on operational stability over social responsibility.

Finally, national and policy frameworks, such as those issued by India's NITI Aayog or the European Union's High-Level Expert Group on AI, attempt to reconcile competing interests by combining human-centered language with economic and innovation-oriented goals. However, the study found that even these hybrid models tend to favor their domestic political and economic agendas.

This comparative perspective shows that AI ethics is deeply political. Rather than representing a universal moral code, ethical principles function as instruments of institutional identity, reflecting how governments, corporations, and research organizations define their roles in shaping AI's future.

Reframing AI ethics for a fragmented global landscape

The study's third question asked whether a global, harmonized AI ethics framework is still possible amid such divergence. The findings suggest that while universal ethical alignment remains aspirational, it may be neither achievable nor desirable in its current form.

The author argues that ethical pluralism is an inherent feature of the AI governance landscape. The challenge, therefore, is not to impose a single framework but to establish "context-aware universality" - a model that respects institutional and cultural diversity while maintaining shared normative anchors such as fairness, transparency, and human dignity.

To move toward that goal, the study calls for deeper cross-sector collaboration between policymakers, academics, and private enterprises. It recommends creating translation mechanisms, not linguistic, but conceptual, that can bridge the gap between technical, legal, and philosophical understandings of ethics. Without such mechanisms, efforts to regulate AI risk remaining symbolic rather than substantive.

The paper also warns against treating ethics as a compliance exercise. Industry's growing reliance on AI ethics boards and guidelines often reduces moral inquiry to procedural checklists, while governments use ethics language to project trust in technological systems without addressing structural inequalities. The author argues that ethics must remain a space of contestation and reflection, not one of bureaucratic routine.

Practically, this means designing sector-specific ethical frameworks that explicitly acknowledge their institutional biases instead of masking them under claims of universality. The author's discourse analysis reveals that transparency about institutional intent could strengthen accountability rather than weaken it.