Move from values to decision evidence
Most organizations can agree that AI should be fair, transparent, safe, and accountable. The harder task is deciding what those values require in a specific context. A human impact review creates a structured conversation before an AI-enabled process gains scale or authority.
The review is not an abstract ethics debate. It examines the real decision, the people affected, plausible benefits and harms, the distribution of those effects, and the mechanisms available to prevent, detect, and remedy failure. This aligns with the OECD AI Principles’ emphasis on human-centered values, transparency, robustness, and accountability.
Map the people and the power
Begin beyond the direct user. Identify the people whose data is used, whose opportunities may change, who must act on the system’s output, who may be burdened by errors, and who lacks influence over design. Include groups that experience the process differently because of language, disability, access, role, or historical disadvantage.
Then examine power. Can a person decline the AI-enabled path? Will they know AI influenced the outcome? Can they reach a human who has authority to correct it? A nominal appeal process is not meaningful if it is inaccessible, slow, or unable to change the decision.
Test the consequential questions
Executives should require clear answers in plain language. Technical performance matters, but accuracy alone does not establish that a system is appropriate for use. Context, process, incentives, and human reliance can turn a capable model into a harmful operating decision.
- What legitimate human or business need does this use serve?
- Who receives the benefit, and who absorbs the error or burden?
- Which failure would be unacceptable even if overall performance is high?
- Where must human judgment remain meaningful rather than ceremonial?
- What notice, explanation, choice, appeal, and remedy will affected people receive?
- What new evidence would cause us to restrict, redesign, or stop the use?
Turn findings into operating controls
A review should end with decisions: changes to scope, data, workflow, thresholds, communications, human oversight, monitoring, or access. Assign each action to an accountable owner and connect it to release criteria. Document accepted tradeoffs so they can be revisited as the system and context evolve.
Repeat the review when the use, model, population, data, autonomy, or consequences materially change. Ethical acceptability is not a permanent property of a model; it is an ongoing judgment about a sociotechnical system in operation.
The essential ethical question is not whether an AI system works in general. It is whether this use, in this context, treats affected people with appropriate care, agency, and accountability.