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Nursing Organisations and AI Governance: Why Human Rights Must Shape the Future of Healthcare Technology


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Cleofas, J. V. (2026). Nursing professional organisations as human rights intermediaries: Towards an integrated framework of stakeholdership for healthcare AI governance. Journal of Clinical Nursing, (Ahead of Print). https://doi.org/10.1111/jocn.70256

What this paper is about

Artificial intelligence is rapidly changing healthcare. AI tools are now used in patient monitoring, imaging, diagnosis, care planning, documentation, scheduling, resource management, patient education, and self-management support.

These changes can bring benefits. AI may help improve efficiency, support clinical decision-making, identify health risks earlier, and make care more responsive.

But AI can also create harm. It can reproduce bias, threaten privacy, weaken patient-provider relationships, create unclear accountability, increase surveillance, reinforce social inequities, and affect nurses’ work, safety, autonomy, and job security.

This paper asks: What can nursing professional organisations do to help govern AI in healthcare?

The answer is not simply “train nurses to use AI.” The paper argues that nursing professional organisations can become active stakeholders in AI governance. They can shape the rules, policies, ethical standards, public debates, professional competencies, and accountability systems that guide how AI is developed and used in healthcare.

The paper’s main contribution is a proposed framework for nursing professional organisations to act as human rights intermediaries in healthcare AI governance.

In simple terms: nursing organisations can help connect the people whose rights must be protected with the institutions that have the duty to protect them.

Why this matters

Nurses are the largest professional group in healthcare. They work close to patients, families, communities, and health systems. Because of this, nurses often see the real-world effects of technology first: what works, what fails, what creates burden, and what creates harm.

But individual nurses cannot carry the whole burden of AI governance alone. A nurse at the bedside may notice that an AI tool is unsafe, biased, confusing, or harmful. But without collective support, it may be hard to challenge the system.

This is where nursing professional organisations matter.

These organisations include professional associations, specialty societies, unions, federations, and other collective nursing bodies. They can speak with a stronger voice than individual nurses. They can issue position statements, organize members, train professionals, lobby policymakers, build coalitions, and advocate for patients and communities.

The paper argues that these organisations should use their collective power to make healthcare AI more ethical, equitable, accountable, and rights-based.

The key idea: nursing professional organisations as human rights intermediaries

The paper’s central concept is human rights intermediation.

A human rights intermediary is an actor that helps connect two groups:

  1. rights claimants — people and groups whose rights must be protected; and
  2. duty bearers — institutions and actors responsible for respecting, protecting, and fulfilling those rights.

In healthcare AI, rights claimants include patients, citizens, marginalized communities, nurses, and other frontline health workers. Their rights include privacy, autonomy, informed consent, equality, non-discrimination, safety, quality care, and the right to health.

Duty bearers include governments, healthcare institutions, regulatory bodies, AI developers, health technology companies, and global health organisations. These actors are responsible for making sure AI systems do not violate people’s rights.

Nursing professional organisations can stand between these groups. They can empower patients and nurses to understand their rights. They can also pressure governments, hospitals, regulators, and technology companies to meet their obligations.

This makes nursing organisations more than professional clubs. They become part of the governance infrastructure for ethical healthcare technology.

The framework: three axes of stakeholdership

The paper proposes a triaxial framework. This means the framework has three axes.

1) Domain: What can nursing organisations influence?

The first axis is domain. This refers to the areas of AI governance where nursing professional organisations can participate.

The paper identifies four domains.

Framing

Framing means defining the problem. Nursing organisations can help identify which AI-related issues matter most for nurses, patients, and communities.

For example, they can ask:

  • What kinds of AI tools are entering nursing practice?
  • Whose needs are being prioritized?
  • What patient safety risks are emerging?
  • How might AI affect nursing judgment, workload, or accountability?
  • Which marginalized groups may be harmed by biased data or design?

Framing matters because the way a problem is defined shapes the solutions that follow.

Risk/benefit assessment

Nursing organisations can help assess the possible risks and benefits of AI tools.

For example, an AI system may promise faster patient assessment. But does it work equally well for all populations? Does it protect privacy? Does it make nurses’ work easier, or does it add documentation burden? Does it support clinical judgment, or does it pressure nurses to follow machine-generated recommendations?

Nursing organisations can bring frontline knowledge into these assessments.

Evaluation

Evaluation means examining whether AI governance policies are actually working.

Nursing organisations can review proposed regulations, compare policy models, assess institutional guidelines, and help determine whether AI oversight mechanisms protect patients and nurses.

Risk management

Risk management means reducing harm.

Nursing organisations can help develop incident reporting systems, best practice guides, certification programs, auditing mechanisms, and public awareness campaigns. They can also advocate for continuous monitoring of AI systems after they are implemented.

2) Modality: How can nursing organisations act?

The second axis is modality. This refers to the ways nursing professional organisations can perform their governance role.

The paper identifies three modalities.

Professional development

Nursing organisations already provide continuing education, training, mentorship, standards, and professional updates. In the AI era, this role becomes even more important.

They can offer training on:

  • AI literacy;
  • algorithmic bias;
  • data privacy;
  • patient safety;
  • ethical use of AI;
  • legal accountability;
  • explainability;
  • responsible documentation;
  • AI-related nursing competencies.

This helps nurses become critical users of AI, not passive recipients of technology.

Organising and mobilisation

Nursing organisations can unite members around shared concerns. They can form special interest groups, conduct member surveys, organize forums, build coalitions, and mobilize nurses when AI systems threaten patient safety or professional integrity.

This matters because AI governance is political. Technology companies, hospitals, regulators, and governments all have interests. Nurses need collective organisation to make sure their expertise and concerns are heard.

Advocacy

Nursing organisations can influence public opinion and policy.

They can issue position statements, submit expert testimony, lobby for legislation, recommend standards, join public consultations, and advocate for patient representation in AI design and ethics boards.

Advocacy ensures that AI policy is not shaped only by technical experts, administrators, or corporations. It also includes the voices of nurses, patients, and communities.

3) Human rights: For whom and toward what end?

The third axis is human rights. This is the moral center of the framework.

The paper uses a Human Rights-Based Approach. This means AI governance should be guided by dignity, equality, non-discrimination, participation, accountability, and the right to health.

The framework highlights several rights that matter in AI-enabled healthcare:

  • Autonomy and informed consent — patients should understand how AI affects their care.
  • Privacy and data security — health data must be protected from misuse.
  • Equality and non-discrimination — AI should not reproduce bias or deepen disparities.
  • Accountability and redress — people should have ways to report harm, seek remedy, and hold institutions responsible.
  • Right to health — AI should improve access, quality, acceptability, and equity in healthcare.

This axis asks the most important questions:
Who benefits from AI? Who is harmed? Who decides? Who is accountable? Who is left out?

What the framework helps nursing organisations do

The framework helps nursing professional organisations move from general concern to strategic action.

For example, in the framing domain, an organisation can hold webinars to help nurses identify AI-related privacy threats, conduct member surveys on AI concerns, or issue a position statement defining the ethical challenges of AI in nursing.

In the risk/benefit assessment domain, it can train nurses to identify algorithmic bias, form expert panels to assess AI tools, or lobby for transparent reporting of AI performance and bias.

In the evaluation domain, it can train nurses to join AI governance committees, build alliances with legal and ethics experts, or publish comparative analyses of AI regulatory approaches.

In the risk management domain, it can create best practice guides, develop certification programs for AI oversight, support incident reporting systems, or campaign for patient rights in AI-driven healthcare.

These actions can empower rights claimants and strengthen duty bearers at the same time.

For example, a nursing organisation may train nurses to identify algorithmic bias. This empowers nurses as rights claimants and patient advocates. But it can also pressure hospitals and technology companies to improve their AI systems.

Why this is important for nurses

The paper is especially important because it protects nurses from being treated as mere users of AI.

If nurses are only trained to operate AI tools, then they become downstream implementers of decisions made elsewhere.

But if nursing professional organisations participate in governance, nurses become co-shapers of AI systems and policies. They can ask whether AI tools are safe, fair, explainable, accountable, and consistent with nursing values.

This protects patients. It also protects nurses.

AI can affect nursing workload, professional judgment, liability, staffing, job security, surveillance, and moral distress. Nursing organisations can help ensure that AI does not become another technology imposed on nurses without their participation.

Bottom line

This paper argues that nursing professional organisations have a major role in healthcare AI governance.

AI is not only a technical innovation. It is a social, ethical, legal, and human rights issue. Because nursing organisations already support professional development, collective action, and advocacy, they are well positioned to influence how AI is introduced, regulated, evaluated, and corrected.

The key message is simple: nursing organisations should help make healthcare AI more human, more just, and more accountable.

Policy/practice recommendations

  1. Create AI governance committees within nursing organisations
    Professional associations and specialty societies can create standing committees focused on healthcare AI, ethics, patient safety, and human rights.
  2. Develop AI literacy programs for nurses
    Continuing professional development should include AI basics, algorithmic bias, explainability, privacy, patient rights, liability, and safe implementation.
  3. Issue position statements on healthcare AI
    Nursing organisations should publicly define their ethical expectations for AI in nursing practice, education, research, and health systems.
  4. Survey nurses about AI-related workplace concerns
    Member surveys can gather frontline evidence about AI tools, workload, patient safety, documentation burden, surveillance, and professional autonomy.
  5. Advocate for nurse participation in AI design
    Nurses should be included from the earliest stages of AI development, not only after tools are already deployed.
  6. Push for patient and community representation
    AI ethics boards, design teams, and regulatory consultations should include patients, marginalized communities, and public representatives.
  7. Develop reporting systems for AI-related harm
    Nursing organisations can advocate for clear pathways to report unsafe AI recommendations, biased outcomes, privacy breaches, and workflow harms.
  8. Build coalitions beyond nursing
    Nursing organisations should work with patients, civil society, ethicists, legal experts, computer scientists, regulators, and technology developers.
  9. Protect nurses from unsupported AI implementation
    Hospitals should not expect nurses to use AI without training, staffing support, legal clarity, and mechanisms for questioning AI-generated recommendations.
  10. Use human rights as the ethical anchor
    AI governance should be evaluated not only by efficiency or innovation, but by whether it protects dignity, equality, autonomy, privacy, safety, and the right to health.

Glossary of key terms

  • Artificial intelligence / AI — Computer systems that can perform tasks usually associated with human intelligence, such as recognizing patterns, generating recommendations, or supporting decisions.
  • Healthcare AI — AI used in healthcare settings, including diagnosis, monitoring, documentation, triage, education, resource management, and care planning.
  • AI governance — The rules, structures, processes, and accountability systems that guide how AI is designed, used, evaluated, and regulated.
  • Healthcare AI governance ecosystem — The network of AI tools, policies, institutions, stakeholders, risks, and accountability mechanisms that shape AI use in healthcare.
  • Nursing professional organisation / NPO — A nursing association, society, union, federation, or specialty group that represents nurses and advances nursing practice, education, standards, policy, and advocacy.
  • Stakeholdership — The actions, involvement, expressions, and resources through which a person or organisation influences, or responds to influence within, a multi-stakeholder network.
  • Human rights intermediary — An actor that helps connect rights claimants and duty bearers to protect and fulfill human rights.
  • Rights claimants — People and groups whose rights must be respected and protected, such as patients, communities, nurses, and frontline health workers.
  • Duty bearers — Institutions or actors responsible for protecting rights, such as governments, healthcare organisations, regulators, AI developers, and technology companies.
  • Human Rights-Based Approach / HRBA — An approach that centers dignity, equality, participation, non-discrimination, accountability, and the rule of law.
  • Right to health — The right of every person to the highest attainable standard of physical and mental health.
  • Autonomy — A person’s right to make informed decisions about their care.
  • Informed consent — Agreement based on clear, understandable, and sufficient information.
  • Data privacy — Protection of personal and health information from misuse or unauthorized access.
  • Algorithmic bias — Unfair or discriminatory outputs produced by AI systems, often because of biased data, design, or implementation.
  • Explainability — The ability to understand how an AI system produces a recommendation or decision.
  • Accountability — The responsibility of people and institutions to explain decisions, correct harm, and answer for failures.
  • Redress — A remedy or response when harm or rights violations occur.
  • Framing — Defining the AI-related problems, priorities, and values that need governance attention.
  • Risk/benefit assessment — Studying the possible harms and benefits of AI systems.
  • Evaluation — Assessing whether AI tools and governance policies work as intended.
  • Risk management — Creating safeguards to prevent, reduce, monitor, and respond to AI-related harms.
  • Professional development — Training, mentoring, education, and competency-building for nurses.
  • Organising and mobilisation — Collective action that unites nurses and partners around shared concerns.
  • Advocacy — Efforts to influence policy, public opinion, standards, and institutional decisions.
  • Co-design — Involving the people affected by a technology, such as nurses and patients, in its design and implementation.
  • Digital health equity — Fair access to safe, useful, and appropriate digital health technologies.
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