EthicsGovernance

AI Ethics Frameworks (AIEF)

In brief: Normative instruments — principles, charters, guidelines, codes of conduct — produced by companies, governments and institutions to govern the development of AI (AI Ethics Frameworks). More than 170 have been published in a decade. Their apparent convergence masks an uncomfortable fact: no study has demonstrated their effect on patient outcomes.

Frame of reference

AIEFs — and this resource that describes them — belong to principlism: the idea, inherited from bioethics, that ethics consists in stating universal principles and then applying them. This top-down approach is a dated and situated philosophical position, not a neutral given. → See other perspectives

Why this concept matters

“Responsible AI,” “compliant with European ethical principles,” “developed under a strict ethics charter”: these labels now appear on nearly all AI tools marketed to mental health professionals — and on the applications your patients already use.

Knowing what these promises actually cover has become a clinical skill. An AI ethics framework is a declaration of principles, almost never a control mechanism. Confusing the two means granting a marketing argument the value of a certification.

This concept gives you the bearings to situate a given charter in the normative landscape (who produced it? with what binding force?), and to ask the questions the charter does not.

The five convergent principles (Floridi & Cowls, 2019)

By comparing the major published frameworks, Floridi and Cowls (2019) showed that their principles converge toward the four classic pillars of bioethics — which you already know from medical and psychological ethics — plus a fifth, specific to AI.

1. Beneficence

AI must benefit people and society. Promote well-being, preserve dignity, sustain the planet.

For the clinician:

Is the stated benefit clinically demonstrated, or merely plausible? “Improves access to care” is a hypothesis, not a result.

2. Non-maleficence

Do no harm: protect privacy, prevent misuse, avoid iatrogenic effects — whether they come from designers or from repurposed uses.

For the clinician:

Who monitors adverse effects once the tool is deployed? Is there an equivalent of pharmacovigilance, or does follow-up stop at market release?

3. Autonomy

Preserve human decision-making power. Floridi speaks of “meta-autonomy”: deciding what one delegates to the machine, and being able to take back control at any time.

For the clinician:

Does the patient know what they have delegated to the application? And do you yourself retain control over the clinical decisions the tool pre-formats (prioritization, alerts, summaries)?

4. Justice

Fairness of access and treatment. Do not amplify existing discrimination, do not create a two-tier mental medicine.

For the clinician:

On which populations was the tool trained and evaluated? A chatbot validated on English-speaking American students tells you nothing about its effects on your patients.

5. Explicability (the AI-specific principle)

Being able to understand how the system produces its results and knowing who is accountable for them. This is the principle that makes the four others verifiable: without explicability, it is impossible to establish an injustice or a harm.

For the clinician:

If the tool directs, alerts or categorizes, can you know why? And in the event of harm, is the chain of accountability (developer, prescriber, clinician) explicit?

Finding your way in the normative landscape

Type of framework	Examples	Binding force
Corporate charter	Google AI Principles, Microsoft Responsible AI	None — voluntary, self-assessed commitment
International recommendation	OECD Principles (2019), UNESCO Recommendation (2021)	Weak — politically binds signatory states
Sector-specific guidelines	HAS-CNIL guide on AI systems in care settings, national digital-health implementation guide	Variable — professional reference, enforceable in practice
Regulation (hard law)	European AI Act (2024), GDPR	Strong — legal obligations, sanctions

About 40% of published frameworks come from technology companies — that is, from the very actors they are supposed to govern. A framework’s position on this continuum entirely changes what its “compliant” label means.

The empirical finding: principles without demonstrated effects

The scoping review by Chan and colleagues (2025, JAMIA) examined a decade of AI ethics frameworks in healthcare: of more than 170 frameworks published between 2014 and 2024, only 16 studies examined their actual implementation — and none established a link between the adoption of a framework and a measurable improvement for patients or organizations.

This gap between declared principles and verifiable practices has a name: the operationalization gap. Brent Mittelstadt (2019) identified its structural cause: unlike medicine, AI has neither fiduciary duties toward its users, nor proven methods for translating principles into practice, nor mechanisms of professional and legal accountability. Principles alone cannot guarantee ethical AI.

When a declaration of principles serves as a showcase without transforming practices, we speak of ethics washing — the normative equivalent of ecological greenwashing.

Illustrative case

A community mental health center evaluates a between-session support application for its patients followed in CBT. The sales materials highlight: “AI developed under an ethics charter compliant with European guidelines, validated by an internal ethics committee.”

Naive reading: the ethics label reassures the team, the application is adopted. Green light.

Informed reading: the lead psychologist asks four questions. Which charter, produced by whom? (The developer’s own — self-assessed.) What concrete mechanisms? (No external audit, no protocol for escalating to a human in a crisis.) What efficacy data? (Satisfaction scores, no clinical measure.) Who is accountable in the event of harm? (The contract is silent.) The ethics label covered none of these questions.

The charter was neither dishonest nor useless — but it described intentions, where the team believed it was reading guarantees. That is exactly the gap the literature documents.

In practice for the clinician

Treat any “ethical AI” label as a declaration, not a guarantee: ask who produced the framework, who verifies its application, and what happens in the event of a breach.
Use the five principles as a set of questions, not as a reassuring checklist: demonstrated benefit? harm monitoring? autonomy preserved? evaluation populations? explicability and accountability?
Rely on sector-specific regulatory references rather than on developer charters: the HAS-CNIL guide on AI systems in care settings and the European AI Act offer more demanding and enforceable benchmarks.
Your professional code of ethics remains the foundation: AIEF principles restate those of bioethics that your code already operationalizes — with, in its case, governing bodies and sanctions. Do not let a technical charter substitute for your professional framework.

What this concept does not say

Interpretive caveats:

There is no single “the” AI ethics framework: AIEF designates a heterogeneous category of more than 170 documents with widely varying content, ambitions and legitimacy
Criticizing frameworks is not rejecting them: they structure public debate and paved the way for the AI Act — the mistake is to take them for control mechanisms
The convergence of principles is nominal: a Chinese framework, a Californian charter and European guidelines may all assert the same “transparency” while designating very different obligations
Principles are not neutral: overwhelmingly produced in North America and Europe, frameworks encode Western conceptions of autonomy and the good — a documented limitation (Birhane, 2021)
Absence of evidence is not evidence of ineffectiveness: Chan et al. show that the effect of frameworks has never been demonstrated — not that it is nil; it is as much an evaluation deficit as an effect deficit

Other perspectives

The principlism of AIEFs presupposes that ethics is a matter of universal principles applied top-down. This presupposition carries biases — depoliticization of the issues, culturally situated universalism — that other traditions make visible.

Ethics of care: the relationship before the principles

For Gilligan and Tronto, ethics does not descend from abstract principles: it arises from concrete relationships of care and from responding to the needs of others. A tool can tick all five principles and still degrade the care relationship.

For the clinician: Evaluate the relational quality produced by the tool, not just its compliance — see the resource Ethics of Care.

Design Justice: who decides, who bears the cost?

For Sasha Costanza-Chock, the real question is not “which principles?” but “who designs, who decides, who bears the consequences?”. Impeccable principles can cover an unjust process from which those most concerned — users, care providers — are absent.

For the clinician: Ask whether patients and clinicians took part in designing and evaluating the tool — a question no checklist of principles asks.

Virtue ethics: disposition rather than rule

For the Aristotelian tradition (taken up by Hagendorff and Vallor for AI), ethics is a disposition cultivated through practice — phronesis, situated judgment — not conformity to rules. A declarative framework makes no one ethical.

For the clinician: Your training, your supervision and your clinical judgment are more effective ethical devices than any charter — which is precisely what AIEFs lack.

These perspectives do not replace principles — they remind us that “which principles?” is only one of the ethical questions. “Which relationships?”, “which decision-making processes?” and “which cultivated dispositions?” are others, which the current AIEF landscape leaves largely unanswered — just as non-Western perspectives (Birhane) remain marginalized in their production.

AI Ethics Frameworks (AIEF)

Why this concept matters

The five convergent principles (Floridi & Cowls, 2019)

1. Beneficence

2. Non-maleficence

3. Autonomy

4. Justice

5. Explicability (the AI-specific principle)

Finding your way in the normative landscape

The empirical finding: principles without demonstrated effects

Illustrative case

In practice for the clinician

What this concept does not say

Other perspectives

Ethics of care: the relationship before the principles

Design Justice: who decides, who bears the cost?

Virtue ethics: disposition rather than rule

Further reading