Our algorithm decided a driver had travelled 4km less than they thought. It was probably right. Shipping that silently would still have been wrong.
When cleaning stops being technical
Mileage tracking ends in an expense claim. Someone drives, the app measures, and the measurement becomes money.
Our pipeline removes several categories of distance: readings from mock location apps, physically implausible jumps, and teleport spikes over five kilometres in a single step. The resulting number is more accurate than the raw sum. That is not in dispute.
The mistake is thinking accuracy is the whole job.
A driver who believes they did 40km and sees 36km cannot distinguish between a careful algorithm and an employer quietly trimming expenses. From where they sit, both look identical: a smaller number, no explanation. Being right is invisible.
Explicit degradation, in the UI
The fix is not better filtering. It is showing the work.
Show the original next to the cleaned figure. Both numbers exist in the data model, so both can appear. The user sees that we measured 40km and are proposing 36km, rather than discovering 36km as if it were the only thing that ever happened.
Name what was removed. Not "adjusted for accuracy" but the actual categories: abnormal distance, mock distance, spike distance. Categories are checkable. Vagueness is not.
Let them overrule it. The trip screen has a toggle for whether the abnormal segment is subtracted:
var final = smartAnalysis.originalDistance
if (removeAbnormal) final -= smartAnalysis.abnormalDistance
This one gets pushback in review. Why let a user override the algorithm? Two reasons. They were physically present and we were not, so on a genuinely ambiguous journey they hold information the pipeline does not. And if our threshold is wrong, this is the cheapest possible feedback channel. The alternative is not that they accept it. The alternative is that they escalate to their finance team, who escalate to us, three weeks later, with no data attached.
Do not cry wolf. In the irregularities view, an app-killed event is explicitly classified as not an irregularity, because the tracker recovers on its own. If every notice is alarming, users learn to dismiss all of them, and the disclosure you worked on becomes noise.
This needs the data model to cooperate
None of this is possible if the pipeline throws away what it rejects. Showing original versus cleaned requires both numbers to survive. Naming the categories requires per-category accumulators. Letting the user toggle removal requires the removed distance to still be sitting there, addable back.
Transparency is a data-modelling decision made months before it becomes a UI decision.
The general case
The pattern applies well beyond mileage. Fraud scores that block a transaction. Ranking systems that decide what a seller earns. Time trackers. Usage-based billing. Anything where a system's output becomes an input to somebody's money, time, or reputation.
For those systems, correctness is table stakes. The actual product is: show the original, show the delta, show the reason, and let them push back.
Being right in silence is just asking to be trusted. Showing the working is how you earn it.
🌀 Iteration 11 of **The Loopdown. field notes from an engineer who writes.
Series: **Chain Of Custody* · Featuring: The Archivist*
← Previously in this series: Your data model is where uncertainty goes to die
📚 The full series: Chain Of Custody
