end field

Chapter 9: Fixing Errors

Following the incident in Zone F, the initial reaction wasn't anger—it was an audit.

Not an emotional audit. A data audit. The council convened earlier than usual. Screens displayed a constant stream of charts, heatmaps, and time series. The numbers moved as if they were breathing.

The chief engineer began with a short statement: “The system has operated according to the new criteria. It has done its job correctly. The problem is: those criteria are incomplete.”

No one argued the word “correct.” Everyone understood. The conflict now lay in detail: what constitutes “complete.”

Medical staff presented patient reports. Two cases required intensive care; both were stable; there were no long-term losses. But the report included a series of risk factors: similar cases, under the same conditions, could potentially increase the loss rate to the reference threshold. That number was highlighted in red, positioned between the columns, not screaming, but staring.

“The system used behavior as the risk distribution variable,” said the logistics team. “That’s the optimal way for productivity. But it missed the demographic variable—that is, the density of chronic patients, the average age, the microclimatic conditions in each area.”

Linh added: “In short: it categorizes people by behavioral history, but not by physiological vulnerability. That’s a bias.”

Several immediate suggestions were made. All seemed reasonable, as they all reduced quantifiable risk:

Add a “vulnerability” parameter to the algorithm; collect health data by area; adjust the priority coefficient.

Create a human-in-the-loop review layer for any changes affecting energy distribution greater than X%.

Set red flags for areas with specific demographic variations; if a red flag is raised, all automated decisions must wait 15 minutes for council review.

Applying behavioral penalties to areas with many blank ballots — temporarily reducing priority if the blank rate exceeds a threshold.

Each option comes with a spreadsheet: benefits — costs — delays. The spreadsheet is viewed like reading a seismic map: each choice shifts an expected amount of loss.

“I want a comprehensive solution,” I said. “Not to make the system better emotionally. But to make it more robust in the face of exceptions.”

The chief engineer nodded. “So we’ll patch in three steps: (1) add medical parameters; (2) human-in-the-loop for decisions exceeding the threshold; (3) record and review behavior to avoid misclassification.”

That was patch V1.1.

Work began immediately. The software team deployed the code. The medical department sent patient density, age, and chronic conditions data in coded form. Logistics adjusted the contingency plans. Linh worked with the user interface to incorporate human handprint alerts into the dispatch screen. Every patch had a 12-step checklist, each step assigned to a specific person, each with a digital signature.

V1.1 was released as a system update: patch notes were concise, devoid of emotional language.

V1.1 — Exception Patch (added medical parameter; Human-in-the-loop for decisions > 5% of distribution; recorded blank ballots in the behavior section for review).

The patch slightly reduced overall performance. Several priority lines now had to wait for human handprint checks, increasing average response time by about 4%. Logistics charted a comparison: downtime decreased; operating time increased slightly. All the numbers were in a justifiable position.

The first to complain wasn't the medical staff. It was the production team leader. “We’re paying for performance,” he said in the meeting. “If we’re 4% behind schedule, this month’s output will drop. In a shortage situation, that 4% is money. Who’s going to compensate?”

The question wasn’t about ethics. It was about cost. People were presented with compensation options: overtime, restructuring schedules, or accepting a temporary reduction in output for safety. The decision was recorded, accompanied by a schedule for workforce adjustments.

The strange thing is: once everything was patched up, the exception became a work item. It was no longer a resistance. It became a checklist.

But what is a checklist? An organizational tool. And a tool easily internalized. We began to believe that by adding a review step, by adding medical data, by increasing processing time—we had fulfilled our ethical responsibility.

In the week following V1.1's launch, there were a few minor events: one area with a high number of blank ballots was reclassified as "special" and therefore lost some of its automatic shipping priority. Another area raised a red flag—all their requests had to wait for manual confirmation, which delayed a food shipment by two hours; residents complained, but not vehemently.

Meetings became more frequent. Everyone on the base took on new roles: programmers became patchers, medical personnel became data providers, logistics became risk managers. I observed their faces during the meetings: no one looked happy, but no one looked guilty either. There was a different—lighter—feeling, like the one born when one completes a crucial task: the results are entered into the system; the system nods; life goes on.

One morning, the security representative came to see me. The gray checkmark was still next to his name on the list. “They t“Add another review item for blank ballots,” he said. His voice was calm. “Now my blank ballots will be noted and reviewed if they appear multiple times.”

“Good,” I replied.

“Or bad?” he asked.

“Both,” I said. “It’s up to you to see.”

He was silent for a moment, then chuckled softly—a silent laugh. “I just hope it doesn’t make me… meaningless.”

“I don’t think blank ballots make anyone meaningless,” I said. “But the system will try to prove it. With data.”

He nodded. “So what will I do?”

“I’ll let the system continue learning,” I said. “And we’ll look at the data. If it’s wrong, we’ll fix it.”

He left, his footsteps heavy as if an extra weight had been placed on his shoulders. I looked at the list; the gray symbol remained there, patiently.

V1.1 didn't fix every blemish. It only smoothed the blemish; it made the edge of the problem smoother for easier acceptance. It widened the gap between action and consequence.

Night fell. I opened the system log. Under patching, a new line appeared:

V1.1 — Deployed. Monitoring impact for 14 cycles.

A useful timeline. A number to hold onto. A short-term commitment.

I turned off the screen.

The exception had been patched. But patching isn't healing. It's a way for a system to say: I've handled it. Keep going. And as the community continues, these little cracks will accumulate, slowly — until they are no longer exceptions.

End of Chapter 9.