Human-in-the-Loop Judgment
Data can discipline judgment without replacing it. Reliable data-driven systems preserve a role for experienced humans to interpret context and override outputs that misrepresent the situation.
The safeguard in a data-driven system may be the person authorized to disobey the data. FutureIQ’s prescription is explicit: experienced humans should examine the output and be allowed to override it.
E1The measurement has no context
Data represents selected features of reality; judgment asks whether those features are sufficient for the decision at hand. A metric can reduce inconsistency and expose patterns, but it cannot independently notice important circumstances excluded from its inputs. The human in the loop supplies experience, interprets the situation, and treats the output as evidence rather than a verdict. This is the operational answer to the McNamara Fallacy: retain measurement while giving someone both the competence and authority to challenge it.
E1An override is not a decorative escape hatch
Human involvement helps only when the reviewer is experienced and genuinely permitted to overrule the system. Otherwise “human oversight” merely adds a person who rubber-stamps the same context-blind output.
E1Define the override before the exception arrives
For one consequential metric or automated recommendation you use, name the experienced person who may override it and require that every override record the missing context. That preserves accountability while revealing what the data repeatedly fails to capture.
E1Episodes that teach this
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Things Go South When You Trust DATA Too Much - Goodhart's Law - FutureIQ
· explained at 9:33
4,228 views
"have humans looking at the data... experienced... allowed to override the data" and "data doesn't understand the full context"