AI ethics concerns how a system affects people and whether its use is justified in the context. It requires decisions about fairness, privacy, accountability and the ability of affected people to question an outcome.
Identify who benefits, who may be harmed and who has little control over the system's use. Examine the actual workflow rather than rely on a general statement of principles. A tool that screens applicants, for example, needs different scrutiny from one that drafts an internal meeting agenda.
Test for relevant disparities and errors using suitable evaluation data. Clarify who can access personal information and how long it is retained. Provide a practical route for correction or review when a system influences an important decision.
Assign responsibility for monitoring the effects after launch. Record tradeoffs and unresolved limitations openly. NIST's voluntary AI risk resources can support structured assessment, but citing a framework does not by itself demonstrate that the system is fair or trustworthy.
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
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Reading adjacent decision areas keeps the topic from becoming an isolated note.