We taught AI to customize – not care. And he should scare us
Since AI systems begin to carry forward, which achieves access to credit, healthcare, jobs, parole, education, even citizenship – cracks.
Listen to the story
I have spent a better part of my professional life at the intersection of technology and change-which is the scaling system, which are retiring, which were not, and are sitting through a long night war room, as much as I care to count. I have worked in rooms that smell like ambition and red bulls.
Apart from all that, a truth has slowly creeped – and recently, it will not leave me alone.
We can teach machines to decide that we are no longer ready for ourselves.
Not because we are wicked. Not even because we are lazy. But because at some point in our race, to scale, to digitize, to adapt, we traded in the decision for efficiency quietly – and did not look back.
And now, as the AI systems begin to move forward, which receives the credits, healthcare, jobs, parole, education, even citizenship – the cracks are showing.
The day I stopped feeling impressed by technology
It was a moment. I remember this clearly.
I was leading a review for an AI-based triage system that helped root internal policy violations. The system was created with good intentions: To reduce bottlenecks, flagged severe cases rapidly, and pressurize human reviewers. On paper, the pilot was a win. It was fast, cheap, more “consistent”. Compliance lead was happy. The metrics were green. The dashboard looked smooth.
And still something was wrong.
I could not shake the understanding of the system – how it determines what was “serious” or “low risk” – not with intentions, but no one now fully understood the proxy variable. On Friday afternoon, a case was green-stimulated by a junior employee at a low-cost center, which was likely to be more. Not due to maliciousness. Because training data said so.
We adapted the wrong thing. And no one had noticed – because the results seemed efficient.
I stopped the rollout. We trained the model again. We lost three weeks. But we achieved our conscience again.
This was the day when I had stopped feeling what technology could do – and started worrying about what we would say to be quietly.
AI does not understand morality. It understands the pattern.
We like to call these systems “intelligent”, but they are not. At least not in any human sense. They do not know what is appropriate. They do not care what is just. They do not wrestle with ambiguity. They do not understand the nuances.
They recognize the pattern. That’s it.
But the patterns they learn are drawn from the past – data which is dirty, biased, uneven and encoded with decisions we are still assuming. And yet we ask these systems to call high-day calls on our behalf, as if they have somehow crossed human defects.
He did not.
In fact, whatever we have done, it increases them.
I have long worked in the scenario of financial services, how well models with well -intentions can produce unexpected results. For example, the credit risk system can learn to down-rank from data-poor areas-not out of the pre-ecstasy, but because the training data reflects historical inequalities. Renting equipment can perform more surecacies on familiar education or location markers. Fraud algorithms sometimes flagged users only because their digital patterns do not get the majority. These results are not the result of maliciousness – they are the result of adaptation without reflection. And because the number often looks good, the deep effects cannot go to anyone until we actively choose to inquire it.
We forget: accuracy is not morality.
Calm erosion of moral accountability
I do not believe that AI is malicious. But I believe this is something that is moral outsourcing.
Here is how this happens.
Step 1: A team creates a system to make a difficult decision easier.
Step 2: A stakeholder says, “If the model recommends it, we can go with it.”
Step 3: The model recommends a recommendation that feels, but no one wants to become a hindrance.
Step 4: This ship.
Step 5: When someone questions this, the answer is, “This is said by the model.”
It seems to be a boring, even boring. But I have seen all the decisions-with some career-transit effect,-one can decrease in a spreadsheet output that has not been fully understood.
Because no one wanted to be accountable to draw the line.
Why does this moment ask for more than policy
AI is a growing chorus calling for regulation. And yes, we need it. Badly.
But the rules will not protect us from deep cultural rot – slow normalization of decisions without any reflection, system without sympathy, adaptation without morality.
Ethics is not a checkbox. This is a practice. A break. The desire to ask difficult questions even when everything looks green in the dashboard.
This hallway appears in conversations. In an analyst who asks, “Why are we using that variable?” In the product lead which pushes the deadline back to the reflection. What the executive says, “We will take a hit on speed, but we will not do anything that does not sit right.”
And it has to be practiced upwards – not after the press cycle deteriorates.
Leaders we really need
I have lost count how many AI panel I am sitting on, where someone says, “We need a moral framework.” We have them. Now what we want is a moral tendency.
It does not come from a toolkit. It comes from culture. Being a different people in the room who experience different alive, which will not notice the things that will not do to others who are prepared as the person who says, “We should not ship it yet.”
I know that the best leaders do not only optimize for the results – they place place for discomfort.
Because inconvenience is the place where the decision remains. And the decision is the place where humanity begins.
Final consideration: If we want morality, we have to make it by hand
AI is going to give more shape to our lives, most of us realize. This is not publicity. He is mathematics.
But as someone is looking at this place and enhances all the discussions, let me say this clearly:
If you do not build morality, you are leaving it by design.
There is no “neutral”. Each line of the code reflects a value. Every feature reflects a tradeoff. Every decision shows a world vision – or one you did not even realize, you have inherited.
The question is not whether we can create intelligent systems.
We already have.
The real question is whether we still have courage – and humility – to remain human while doing it.
Because the moment we stop asking, we need us, and only we can ask, we have already handed over more than what we should do.
