Ethical AI Standards

Ethical AI Standards

The Foundation for Ethical AI establishes these standards to define what ethical AI means in practice, not as aspiration, but as operational responsibility.
These standards are intended to guide the development, deployment, and governance of AI systems whose failure, misuse, or opacity would result in material harm.

Ethical AI is not a branding claim.
It’s a duty of care.

Scope

These standards apply to:

  • AI systems used in regulated, high-impact, or mission-critical contexts

  • Organizations developing, deploying, or licensing AI technologies

  • Partners, vendors, and data stewards operating under the Foundation’s ethical framework

The standards are technology-agnostic and jurisdiction-aware, designed to evolve as AI capabilities and risks change.

Core Principles

1. Accountability

Every AI system must have clearly defined human accountability.

  • Responsibility for outcomes cannot be delegated to the model

  • Ownership must be traceable across the AI lifecycle

  • Accountability persists post-deployment

Ethical AI requires someone answerable at all times.

2. Transparency (Contextual, Not Absolute)

AI systems must be transparent to the degree necessary for governance, audit, and trust, not indiscriminately open.

  • Decision logic must be explainable to relevant stakeholders

  • System limitations must be disclosed to partners

  • Transparency obligations vary by use case, risk level, and audience

Ethics requires appropriate visibility, not performative openness.

3. Governance & Oversight

Ethical AI systems operate within defined governance structures.

  • Clear policies for use, escalation, and exception handling

  • Regular internal review and risk assessment

  • Defined processes for responding to failures or unintended outcomes

Un-governed AI is not ethical AI.

4. Risk Awareness & Harm Prevention

AI systems must be evaluated not only for capability, but for consequence.

  • Identification of foreseeable misuse or downstream harm

  • Mitigation strategies proportionate to risk

  • Ongoing monitoring, not one-time assessment

Ethical AI anticipates harm, it does not wait for it.

5. Human-Centered Design

AI must augment human decision-making, not obscure or replace human judgment in high-stakes contexts.

  • Humans retain authority in consequential decisions

  • AI outputs must be contextualized, not treated as truth

  • Systems must respect human dignity and agency

Ethical AI serves people, it does not subordinate them.

6. Integrity of Inputs

AI systems are only as ethical as the materials used to build them.

  • Inputs must be lawfully obtained and responsibly sourced

  • Origins must be known, documented, and defensible

  • Ethical responsibility does not end at acquisition

Opacity at the foundation creates risk at the surface.

7. Stewardship Over Exploitation

Ethical AI treats systems, data, and outputs as long-term responsibilities, not disposable assets.

  • Lifecycle thinking over short-term gain

  • Continuity, maintenance, and responsibility over time

  • Respect for the long-term impact of AI deployment

Ethics is sustained behavior, not a launch condition.

8. Adaptability & Continuous Review

Ethical AI standards must evolve.

  • Regular reassessment as technology and context change

  • Willingness to revise practices when risks emerge

  • No claim of permanent ethical completeness

Ethical AI is a process, not a certification moment.

Application

These standards guide:

  • The Foundation’s internal initiatives

  • Affiliated platforms and partners

  • Ethical evaluation of AI systems and practices

Alignment with these standards indicates a commitment to responsible AI, not perfection, but accountability.

The Foundation for Ethical AI does not claim authority through consensus or popularity.
It claims authority through clarity, responsibility, and application.

Ethical AI is not what a system claims to be.
It is how it is governed, stewarded, and answered for.