AI can scan data, find patterns, and compare possible outcomes. A decision involves more. The organization's purpose, values, relationships, risk appetite, and the impact on people all come into play. A model can produce a recommendation, but it cannot be held accountable for it.
The outputs themselves have problems too. Hallucinations, missing context, outdated information, and bias originating in the data are common forms of error. The list of human errors is hardly short either: confirmation bias, overconfidence, authority effects, and habits. I treat a human-in-the-loop approach as a controlled decision-making system that recognizes how both sides make mistakes, without assuming that people are flawless judges.
In this system, human responsibility begins well before the end. It runs through defining the problem, questioning the limits of the data, examining the model's assumptions, and rejecting its recommendation when necessary. For high-impact decisions, this also means seeking an independent second opinion, a counter-scenario, and a traceable decision record.
Speed and care both need attention. There can be more room for automation in low-risk, reversible decisions. When human life, reputation, rights, financial loss, or strategic direction is at stake, human judgment needs to enter earlier and carry greater weight.
Ask yourself
- What context in this decision is invisible to AI?
- Which biases of the model and the decision-maker could reinforce one another?
- Who bears the consequences of a wrong decision, and who can explain the decision?
One way to start building this system is to classify decisions by their level of impact, uncertainty, and reversibility. For each category, it is necessary to document in advance where human intervention begins, what evidence will be required, and who will be accountable. After a decision, reviewing the outcome, any deviation, and the rationale together makes it clearer which signals to look for in the next decision.
