An automated decision combines a prediction with a choice about what to do to a person. Even a technically accurate model cannot decide which errors are acceptable or what explanation someone is owed. The ethical task includes the objective being optimized, the data used, and the institution responsible for the result.
A prediction is only one step in an institutional decision. A model might estimate a risk, while people choose the threshold, the consequences of crossing it, and the process for reconsideration. Each choice distributes burdens differently. False rejection may mean lost support for an applicant, while false acceptance may consume shared resources. Calling the system accurate does not answer whose errors are tolerated, who can contest them, or who remains accountable for the policy.