The AI Ethics Framework provides a structured approach to evaluating the ethical implications of AI systems throughout their lifecycle. It moves ethical considerations from abstract principles to concrete, actionable review processes that integrate into the AI development and deployment workflow.

Framework Principles

Beneficence - AI systems should create genuine value for their intended users and broader society. The expected benefits must be clearly articulated and measured, not assumed. If the primary beneficiary of an AI system is the deploying organization rather than the people affected by its decisions, additional scrutiny is warranted.

Non-maleficence - AI systems should not cause harm to individuals, groups, or society. Harm includes direct harm (incorrect medical advice), indirect harm (discriminatory outcomes), and systemic harm (erosion of human agency through over-automation of consequential decisions). The absence of intended harm is not sufficient; organizations must actively identify and mitigate foreseeable harms.

Autonomy - AI systems should respect human agency and decision-making capacity. People affected by AI decisions should be informed that AI is involved, understand how it influences outcomes, and have meaningful recourse when they disagree with an AI-driven decision. Fully automated consequential decisions without human oversight require the strongest justification.

Justice - AI systems should distribute benefits and burdens fairly. Performance should be equitable across demographic groups, geographies, and socioeconomic segments. When disparities exist, they must be documented, justified, and mitigated where possible.

Explicability - AI system behavior should be explainable to the degree appropriate for the context. High-stakes decisions require detailed explanations. Low-stakes recommendations may require only general transparency about AI involvement.

Ethical Risk Assessment

For each AI system, conduct a structured ethical risk assessment covering:

Stakeholder identification - Who benefits from this system? Who bears risks? Who is excluded from benefits? Are any groups disproportionately affected? Include direct users, subjects of AI decisions, affected communities, and future generations where relevant.

Impact analysis - What are the potential positive and negative impacts on each stakeholder group? Consider impacts on autonomy, dignity, privacy, economic opportunity, safety, and social cohesion. Assess both intended impacts and foreseeable unintended impacts.

Power dynamics - Does the AI system concentrate power or distribute it? Does it create information asymmetries between the deploying organization and affected individuals? Does it reduce or enhance the bargaining position of vulnerable parties?

Reversibility - Can the effects of an incorrect AI decision be reversed? Denying someone a job interview based on AI screening is partially reversible (they can reapply). A medical misdiagnosis that delays treatment may cause irreversible harm. Less reversible impacts demand more rigorous ethical review.

Decision Framework

When ethical concerns are identified, apply this decision hierarchy:

  1. Eliminate the risk - Can the AI system be designed to avoid the ethical concern entirely? Removing a problematic feature or data input is preferable to mitigating its effects.

  2. Mitigate the risk - If elimination is not feasible, what technical and operational safeguards reduce the likelihood and severity of harm? Guardrails, human oversight, monitoring, and appeal mechanisms.

  3. Justify the residual risk - If mitigation reduces but does not eliminate the risk, is the residual risk proportionate to the benefits? Document the justification explicitly.

  4. Decline to proceed - If the residual risk is disproportionate to the benefits, do not deploy the system. The ability to say no is the most important power an ethics review process has.

Integration into Development Lifecycle

Ethical review should not be a single gate at the end of development. Integrate ethical checkpoints at project inception (is this use case ethically appropriate?), design (are ethical risks being designed out?), development (are fairness metrics being tracked alongside performance?), pre-deployment (does the system pass ethical review criteria?), and post-deployment (are ethical commitments being met in practice?).

Document ethical review decisions and their rationale. When ethical tradeoffs are made, record who made the decision, what alternatives were considered, and what monitoring is in place to verify that the tradeoff remains acceptable over time.