Human Error, Broken Workflows, and the Online Pile-On
Two case studies in systems design, operations, AI accountability, and what happens when public criticism becomes entertainment
Last Saturday gave me two useful case studies.
The first happened at NAIA.
The second happened on Facebook.
Both were about human behavior. Both exposed weak systems. Both reinforced a lesson I keep returning to in operations:
Human error is predictable. Good systems account for it.
Case Study One: The Bag-Drop Failure
I was traveling through NAIA and using the self-service bag-drop system.
I made a human error.
The bag entered the conveyor without its tag attached.
My bad. I owned that immediately.
The larger operational issue remained: the system accepted the bag, the retrieval process took hours, and our flight was placed at risk.
A high-stakes workflow should account for predictable human error before the consequence becomes expensive.
This principle also applies to AI workflows.
Human accountability remains essential for any AI-assisted output. A team cannot simply point to the user, the model, or the interface and end the analysis there.
In Ops, we do not improve systems by assuming every user will execute every instruction perfectly.
We examine:
where the mistake became possible
why the workflow allowed it to continue
which safeguard failed or was missing
how quickly the team detected and recovered from the error
whether the user received clear instructions and escalation support
That is how incident reviews work.
Blaming the user ends the analysis too early.
A strong process asks a harder question:
Why was one missed step allowed to create such a large operational consequence?
Case Study Two: The Online Pile-On
I posted about the incident on Facebook.
The response became a second case study.
Many people repeated the same joke I had already made about being a “pro traveler.” Others moved onto unrelated photos, mocked my intelligence, and treated a stressful incident as public entertainment.
That is a pile-on.
A pile-on happens when criticism gains momentum and more people join because participation earns attention, reactions, and social approval.
The original issue becomes secondary.
The crowd becomes the content.
What fuels a pile-on
1. Status policing
People try to prove that someone is careless, dramatic, privileged, arrogant, or wrong.
2. Schadenfreude
Someone else’s embarrassment becomes entertainment.
3. Crowd performance
People write for laughs and reactions instead of adding anything useful.
4. Low accountability
Strangers say things online they would rarely say face-to-face, often from empty or fake profiles.
5. Mistake absolutism
One error becomes evidence that the person’s entire argument, career, or character should be dismissed.
6. Context collapse
A stranger’s whole identity gets judged from one stressful moment.
7. Algorithmic amplification
Platforms reward outrage because outrage drives comments, shares, viewing time, and ad inventory.
This is where the second systems lesson appears.
Social platforms are built to keep attention moving. Negativity is highly effective at doing that.
A thoughtful discussion about workflow design may receive a few comments.
A sarcastic insult can trigger hundreds.
The platform learns what keeps people watching and gives them more of it.
What AI Workflows Can Learn From This
Well-designed AI workflows are meant to reduce careless judgment.
They should use context, structured checks, review steps, escalation rules, and human approval.
A good AI system should not take one mistake and use it to dismiss the entire person.
Humans, unfortunately, often do exactly that online.
So are machines already better than people?
Not quite.
AI still produces errors, misses context, and requires human review.
But the comparison exposes something uncomfortable:
A properly designed AI workflow can be more disciplined than an online crowd.
It can pause.
It can check.
It can ask for missing context.
It can follow a defined escalation path.
A pile-on does none of those things.
What to Do When a Pile-On Starts
Document
Screenshot comments, usernames, profiles, timestamps, threats, and activity on unrelated posts.
Keep records before hiding or deleting anything.
Secure
Limit access to your friends list, old posts, tags, mentions, workplace information, contact details, and personal photos.
Review which posts are public.
Moderate
Hide, restrict, block, and report.
A personal profile is not a public hearing where every stranger is entitled to a microphone.
Moderation is part of operating your own space.
Step Away
Once the crowd stops discussing the issue and starts performing for one another, further explanation rarely helps.
Protect your attention.
The algorithm has already taken enough.
The Two Operational Lessons
The airport incident and the online response came from different systems, but the lesson was the same.
Human error is predictable. Build safeguards around it.
Online cruelty is predictable. Build boundaries around that too.
A system should be judged by what happens when someone makes a mistake.
A community should be judged by what it does when someone is already having a difficult day.
As Odysseus would say:
“Take courage, my heart: you have been through worse than this.”
— Homer, The Odyssey
