Now Nobody Is Responsible
🔒 Part of the Trust the Vault Collection — What you share. Who can see it. What protections actually exist.
The old political dodge required effort.
You needed a straight face.
A rehearsed explanation.
A carefully crafted excuse.
When something went wrong, somebody had to stand in front of a microphone and explain it.
Today, there is a new option.
Blame the algorithm.
The Perfect Scapegoat
Artificial intelligence has created the perfect employee.
It never complains.
Never takes vacation.
Never hires a lawyer.
And most importantly:
It cannot defend itself.
That makes it the ideal scapegoat.
“We didn’t make that decision.”
“The algorithm did.”
“The system flagged your account.”
“The AI recommended it.”
The explanation sounds technical enough to discourage questions.
Expertise and authority are not always the same thing. See The Expert Problem.Responsibility Has Become Fuzzy
In the past, responsibility was relatively straightforward.
A person made a decision.
A person accepted the consequences.
Today, decisions often travel through layers of software, automation, recommendations, scoring systems, and machine learning models.
When something goes wrong, everyone points somewhere else.
The employee blames the software.
Management blames the vendor.
The vendor blames the data.
The data scientists blame the users.
The users blame the company.
The company blames the algorithm.
And the algorithm remains suspiciously silent.
We’ve already seen examples of people blaming AI when outcomes don’t go their way. See Loser Fatigue: The AI Scapegoat and the ChatGPT Bar Exam.“The algorithm may assist the decision. Responsibility still belongs to people.”
The Appeal of Automation
Automation is not the problem.
Most automated systems work remarkably well.
The problem begins when automation becomes an excuse.
A recommendation becomes a decision.
A suggestion becomes a policy.
A probability becomes a verdict.
People stop questioning outcomes because a machine was involved.
Technology creates an illusion of objectivity.
Looking objective and being objective are not always the same thing. See The Appearance of Competence.Sometimes deserved.
Sometimes not.
The Authority Effect
Many people assume computer-generated decisions are inherently neutral.
That assumption can be dangerous.
Algorithms reflect:
- training data
- design choices
- business incentives
- human assumptions
Machines do not eliminate bias.
Incentives shape outcomes long before algorithms enter the picture. See The Incentive Problem.They often automate it.
At scale.
The Customer Service Maze
Most people have already experienced the AI Alibi Age.
A bank freezes an account.
An insurance claim is rejected.
A social media account disappears.
A payment gets flagged.
Nobody can explain why.
The explanation becomes:
“The system detected unusual activity.”
What activity?
Nobody knows.
Who made the decision?
Nobody knows.
How do you appeal?
Good question.
The New Accountability Problem
Technology has made many things faster.
It has not made accountability easier.
In fact, it may have done the opposite.
The more layers between a decision and the person affected by it, the harder it becomes to identify who is responsible.
Complexity becomes cover.
Modern systems increasingly depend on infrastructure few people fully understand. See The Age of Invisible Infrastructure.The Trust Problem
People generally accept mistakes.
What they dislike is confusion.
If a human being makes a bad decision, there is at least someone to question.
When responsibility disappears into a black box, trust disappears with it.
People begin to suspect the system itself.
Often with good reason.
The Better Question
When an organization says:
“The algorithm made the decision.”
Ask:
“Who designed the algorithm?”
Then ask:
“Who approved its use?”
Technology may assist decisions.
Responsibility still belongs to people.
The Bunker Rule
The moment nobody is responsible, everybody should be concerned.
CHATRODAMUS OBSERVATION
Technology can automate decisions.
It cannot automate responsibility.
Someone always chose the rules.
BUNKER NOTICE
When somebody tells you, “The system made the decision,” don’t stop asking questions.
Systems do not create themselves.
People build them, train them, deploy them, and profit from them.
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