AI Makes It Hard to Hide and Harder to Stay Safe

As long as markets have existed, people have profited from what others cannot see. The seller knows the car has been in an accident but says nothing. The employee works hard until the job is secure, then eases off. The insurance claimant stretches the truth. The manager buries bad news in the footnotes of a quarterly report.

The details vary, but the core advantage remains the same: I know something you do not.

Economists have long understood this problem. Moral hazard arises when people take risks or act opportunistically because they do not bear the full consequences of their actions. Information asymmetry can exacerbate the problem because one party in a transaction often has more information than the other party.

This is not simply about dishonest people. It is about incentives. If behaviour is hard to observe and the consequences fall partly on someone else, even rational people can make decisions that lead to bad outcomes.

For generations, we have tried to control this through contracts, audits, disclosure rules, incentives, and professional ethics. Hold managers accountable. Audit the books. Require companies to disclose material information. Reward good performance. Punish fraud. All these mechanisms matter. But they share a fundamental limitation: what cannot be seen is difficult to govern. Artificial intelligence is beginning to change that, which may be both good and bad news.

AI is closing the information gap

Much opportunistic behavior depends on an information advantage. The employee knows how much effort they are really putting in. The seller knows more about the product than the buyer. The borrower knows more about their circumstances than the lender. The company knows more about its operations than the regulator. AI, combined with the extraordinary expansion of digital data, can narrow some of these gaps. Not because AI makes us more honest, but because it makes hiding harder.

Consider the used car. A seller who failed to maintain a vehicle or disclose damage once held a considerable advantage. Unless the buyer commissioned an expert inspection or obtained reliable records, much of the car’s history remained hidden. Increasingly, service histories, insurance records, vehicle diagnostics, sensor readings and other digital traces can be analyzed together. AI can identify inconsistencies and patterns that an ordinary buyer, or even a human inspector, might miss.

The informational advantage begins to shrink. The same trend is unfolding across the economy.

Financial institutions use machine learning to analyze enormous volumes of transactions and identify unusual patterns. Healthcare systems can detect anomalies in billing, prescribing and treatment. Supply-chain technologies can compare what companies say is happening with what is actually moving through factories, warehouses and ports. Corporate disclosures can increasingly be tested against operational and market data.

AI does not provide perfect transparency. Data can be incomplete, biased, manipulated, or simply wrong.

But something fundamental is changing. Verification is becoming cheaper, faster and more continuous.

For centuries, economic governance has largely relied on periodic inspections: the annual audit, the quarterly report and the scheduled performance review. AI brings us closer to continuous verification. That could transform markets, but it also creates a paradox.

When the watcher becomes the decision-maker

The same AI systems that make human behavior easier to observe are increasingly making consequential decisions. They rank job applicants, flag insurance claims, assess borrowers, identify suspicious transactions, recommend treatments, allocate resources and determine what information people see.

The watcher is becoming an agent. And that changes the old principal-agent problem.

For generations, the question has been: How do we ensure the human agent acts in the principal’s interests?

Increasingly, the question is: How do we ensure the algorithm acts in accordance with human intentions, institutional responsibilities, and the rights of the people affected by it?

That question is much harder than it sounds. An AI system does not understand our intentions the way another person might. It optimizes according to the objectives, data, and constraints embedded in its design and deployment.

But what we can measure is rarely the same as what we value. A bank wants responsible lending, but its algorithm sees predicted default risk. A company wants a productive and sustainable workforce, but its system measures output. An insurer wants to identify fraud, but its model measures how closely a claim resembles cases previously labeled suspicious. That difference matters. The metric is not the mission.

An algorithm designed to reduce loan defaults may improve its performance by excluding applicants whose characteristics are associated with higher historical risk, potentially reproducing past inequalities.

A productivity system may increase short-term output while encouraging practices that lead to burnout. A fraud-detection system may focus scrutiny on particular communities because historical data show those communities were investigated more often in the past.

The machine need not be malicious for any of this to happen. Indeed, that is precisely the point. It may be doing exactly what we asked it to do. The danger is not machine malice. It is misalignment at scale.

AI has no skin in the game

There is an even deeper problem. Human decision-makers can, at least in principle, be held responsible. A doctor can lose a license. A director can be dismissed. An auditor can be sanctioned. An executive who commits fraud can be prosecuted. Reputation, career, conscience and the law all connect human decisions to consequences.

An algorithm has none of these things. It has no career to lose, no reputation to protect, and no conscience to trouble. It cannot be embarrassed, dismissed or imprisoned. So responsibility cannot rest with the machine. It must remain with people and institutions.

Yet automation can make that responsibility surprisingly difficult to pinpoint. Imagine a harmful decision produced by a system involving a model developer, a data provider, an external vendor, an institutional manager and an automated decision engine.

Who is responsible? The developer says the institution decided how to use the model. The institution says the vendor built it. The manager says the algorithm produced the recommendation. The vendor points to the training data. Everyone participated, yet nobody appears responsible.

This may become one of the defining moral hazards of the AI age: responsibility without a clearly defined responsible party.

We must govern the watchers

This is why AI governance cannot be reduced to regulating code. Traditional audits ask whether financial statements accurately reflect what happened. Algorithmic governance must ask much more. What is the system actually optimizing? What proxies is it using? Who benefits when it succeeds? Who pays when it fails? Which values have been omitted from its objective function? Can someone affected by its decision challenge it?

And above all, who is accountable? We therefore need to audit not only algorithms but also objectives, data, incentives, outcomes and responsibility.

High-impact AI systems should have clearly identified human and institutional owners. Their objectives and key constraints should be documented. Their outcomes should be assessed not only for accuracy and efficiency but also for systematic harm.

People affected by consequential automated decisions should have meaningful ways to challenge them. Independent auditing should ask not only whether an algorithm does what it was designed to do, but also whether that purpose remains acceptable.

Human oversight must mean more than placing a person at the end of an automated process and asking them to approve whatever the machine recommends. A human rubber stamp is not human accountability. Accountability cannot be automated away.

AI may make markets more transparent than ever. It may expose fraud, reveal hidden risks and make opportunistic behaviour considerably harder to sustain. That is progress. But transparency and accountability are not the same thing.

We may be moving from an economy in which the central problem was that people could hide what they were doing to one in which powerful systems can see almost everything, while responsibility for what follows becomes harder to assign.

That is the paradox of the AI age: More visibility, less hiding, but potentially less accountability.

The challenge, therefore, is not simply to build machines capable of watching us. It is to ensure that the watchers themselves can be watched, questioned and governed.

In a world where AI makes it harder for anyone to hide, there is one actor we must never allow to disappear: the human being, or institution, responsible for the decision.