When Information Survives but the Truth Doesn't
By John R. Kennedy ·
Why preserving information isn't the same as preserving its meaning
I've spent much of my professional life helping businesses protect their information. Over the years, the technology has changed considerably, but the underlying objective has remained relatively consistent. We want to prevent important information from being lost, stolen, or made unavailable when someone needs it. We create backups, implement security measures, and develop procedures intended to ensure that organizations can continue operating when something goes wrong.
These are important responsibilities, and I've seen firsthand how disruptive it can be when information suddenly becomes inaccessible. But as I've spent more time thinking about the relationship between information and artificial intelligence, I've begun questioning an assumption that has quietly influenced much of our approach to protecting data.
We tend to believe that if information survives, we've successfully preserved it.
I'm no longer convinced that's always true.
Consider a business that has maintained its records for thirty years. Its documents have been backed up, its databases carefully protected, and its correspondence retained according to established procedures. From a traditional information technology perspective, the organization might have done nearly everything right. Yet imagine that many of the employees responsible for creating those records have retired, the circumstances surrounding important decisions have been forgotten, and the meaning of certain documents is no longer entirely clear.
The information still exists, but the organization may no longer understand what it possesses.
This distinction becomes particularly interesting when we consider how information acquires meaning in the first place. A document doesn't necessarily explain everything about the circumstances that produced it. An email may contain a decision without recording the conversations that led to it. A financial report can accurately describe what happened while revealing very little about why it happened.
For the people involved at the time, these limitations may not matter. They remember the circumstances, understand the relationships, and recognize which details were considered important. Years later, someone examining the same information may reach an entirely different conclusion, not because the original record was inaccurate, but because the knowledge needed to interpret it has disappeared.
I've encountered versions of this problem in business more often than I can count. An organization may have a procedure that everyone follows without anyone remembering why it was established. Sometimes the procedure remains necessary because it addresses a problem that could still occur. Other times, the circumstances have changed so significantly that the procedure no longer serves its original purpose.
The written instructions might be perfectly accurate, yet their continued application could produce the wrong result.
What makes this difficult is that the document itself provides no obvious warning. It doesn't announce that the assumptions behind it are outdated or that the person who wrote it expected the reader to possess knowledge that is no longer available.
Until recently, interpreting these records depended almost entirely upon people. Someone had to recognize that something didn't make sense, investigate the circumstances, and determine whether the information could still be trusted.
Artificial intelligence is beginning to change that process.
We're developing systems capable of examining enormous collections of documents, identifying relationships between records, and producing explanations that appear to reflect an understanding of the information they've encountered. This creates tremendous opportunities for organizations that have accumulated years of knowledge without having practical ways to use it.
But it also introduces a problem that I think deserves more attention.
An AI system can become very good at explaining information without necessarily knowing whether that information deserves to be believed.
Imagine a company that uses AI to help employees answer questions about its internal procedures. The system searches the organization's documents, finds what appears to be a relevant policy, and provides a clear explanation of the steps employees should follow. It might even identify the document it used, allowing the employee to verify that the explanation accurately reflects the written instructions.
Everything appears to be working correctly.
But what happens if the document was created fifteen years earlier, the policy was informally abandoned five years ago, and nobody ever updated the original record?
The AI may have accurately represented the information it found while providing advice that is no longer appropriate.
This isn't necessarily a failure of the AI's ability to understand language. The problem is that the system may have no reliable way to distinguish between information that exists and information that remains authoritative.
I find that distinction important because we often use the word truth as though it describes a permanent characteristic of information. In reality, many things we consider true depend upon circumstances that can change.
A customer's mailing address might be correct today and incorrect tomorrow. A medical instruction may be appropriate for one person but dangerous for another. A business policy may be valid for a particular department while being completely irrelevant to someone working elsewhere.
Even historical information, which we might expect to remain unchanged, can become misleading when separated from the circumstances in which it was created.
An old photograph provides a familiar example. Suppose you discover a photograph of several people standing outside a building. Someone has written a date and a few names on the back, and the photograph has been carefully preserved for decades.
You might reasonably assume that the information accompanying it is accurate. But perhaps one of the names was written incorrectly, or the date reflects when the photograph was developed rather than when it was taken. Maybe the building belonged to a different organization at the time, or the people pictured were attending an event unrelated to what a family member later assumed.
None of these possibilities changes the photograph itself. Yet each could substantially change the story someone tells about it.
Now imagine that an AI system uses the photograph and its accompanying description to help construct a family history. It identifies connections with other records, produces a compelling narrative, and presents the result in language that sounds authoritative.
If the original description contained an error, that error may become part of a much larger explanation. And because the explanation connects multiple pieces of information, it may appear more convincing than the original record ever did.
This is one reason I believe artificial intelligence will force us to reconsider what it means to preserve information responsibly.
Historically, much of our attention has focused on ensuring that information remains available and hasn't been improperly altered. We want to know that a document is authentic, that a database hasn't been corrupted, and that a backup can restore what was lost.
Those concerns remain essential, but they address only part of the problem.
A document can be authentic and still be wrong. It can be accurate when created and misleading years later. It can faithfully record someone's words without preserving what that person meant.
And perhaps most importantly, it can be technically available to an AI system without being appropriate for that system to use.
This brings me back to the idea that everything is an asset.
When we think about the value of an asset, we usually consider more than whether it physically exists. A house may still be standing, but its condition, location, and legal status influence what someone can reasonably do with it. A vehicle may be perfectly functional while lacking the documentation necessary to establish ownership.
Information has similar characteristics, although we've rarely treated them with the same attention.
Its value depends partly on whether it's accurate, but also on where it came from, when it was created, what it describes, and whether the circumstances surrounding it remain relevant. It may also depend on who is permitted to access it and whether the person or system using it understands the limitations associated with it.
A business might possess a detailed customer record that is entirely accurate but was collected for a specific purpose. That doesn't automatically mean the business should use the information for every new application it develops. The record's accuracy doesn't eliminate the responsibilities associated with how it was obtained or the expectations of the person who provided it.
Similarly, an organization might have access to an employee's historical performance evaluations. Those records may accurately describe particular events, but they might not provide a fair representation of the employee's current abilities or circumstances. Using them without understanding their limitations could produce conclusions that are technically supported by documentation but fundamentally misleading.
These examples suggest that the quality of information involves considerably more than the absence of errors.
I think about this in terms of the difference between possessing information and understanding the conditions under which it remains useful.
People make these distinctions constantly, often without realizing it. When a friend tells us something surprising, we consider what we know about that person, the circumstances surrounding the conversation, and whether there might be another explanation. When a longtime employee questions a procedure, we may give their concern additional weight because we understand their experience and judgment.
We don't evaluate information solely by examining the words or numbers in front of us. We draw upon relationships, history, and context that help us decide how much confidence to place in what we're being told.
Artificial intelligence can potentially help us make similar evaluations, particularly when it has access to reliable information about where records originated and how they've changed over time. But there's a danger in assuming that an AI system possesses the same understanding simply because it can produce a convincing explanation.
The system may identify relationships that humans would never have noticed, yet still fail to recognize a missing piece of context that someone familiar with the situation would consider obvious.
As AI becomes more capable of taking action, these limitations become more consequential.
An incorrect answer to a question may cause confusion or inconvenience. An automated system acting upon incorrect information could make decisions, alter records, or communicate with people before anyone recognizes the underlying mistake.
Consider an AI assistant responsible for helping a business maintain customer relationships. It might examine historical correspondence to understand customer preferences and identify opportunities to provide better service. Used carefully, this could be extremely valuable.
But suppose an old email contains a complaint that was later resolved through a telephone conversation that was never documented. The assistant might conclude that the customer remains dissatisfied and begin taking actions based upon a situation that no longer exists.
The problem isn't necessarily that the AI misunderstood the email. It may have understood the available information perfectly. What it lacked was the information necessary to understand the relationship.
This is where I believe the concept of information quality becomes inseparable from human judgment.
We can develop increasingly sophisticated systems for organizing records, tracking changes, identifying sources, and determining who has authority to access particular information. These capabilities will become essential as AI systems take on greater responsibility.
But we also need to recognize that no collection of technical controls can guarantee that every relevant circumstance has been recorded.
Human lives and organizations are too complicated for that.
People change their minds. Relationships evolve. Decisions are reconsidered. Circumstances shift in ways that may never be formally documented, and sometimes the most important information is something nobody thought to record.
I don't believe this means we should avoid using AI to understand our information. I believe it means we should become more thoughtful about the difference between what a system knows and what it has reason to believe.
An AI assistant that identifies uncertainty, recognizes conflicting records, and asks for clarification may ultimately be more valuable than one that confidently produces an answer to every question.
That might seem counterintuitive in a world where we're increasingly impressed by systems that can accomplish complex tasks without human intervention. Yet the ability to recognize when information is insufficient could become one of the most important characteristics of trustworthy artificial intelligence.
I've begun thinking about how different our approach to information might become if we treated its history and limitations as part of the asset itself.
Instead of simply preserving a document, we might preserve enough information to understand who created it, why it was created, and whether it has been replaced or challenged. Rather than assuming that a record remains valid indefinitely, we might develop ways to recognize when its usefulness depends upon circumstances that should be reconsidered.
For individuals, this could mean preserving the stories behind important photographs, documents, and family decisions. For businesses, it could mean retaining more of the reasoning behind procedures and recognizing that institutional knowledge is often distributed among people rather than contained within formal records.
These aren't entirely new ideas. Organizations have long used version histories, audit trails, and recordkeeping practices to establish accountability. What changes with artificial intelligence is the potential importance of those details to systems that may interpret and act upon information at enormous scale.
The better these systems become at using information, the more consequential our assumptions about that information are likely to become.
There's an irony in this that I find particularly interesting.
For years, we've been concerned about losing information because technology failed to preserve it. Now we're approaching a future in which technology may preserve more information than any previous generation could have imagined, while making it increasingly difficult to distinguish between what was recorded, what was inferred, and what was actually understood.
We may eventually find ourselves surrounded by information that is readily available, carefully protected, and convincingly explained, yet still struggle to determine which conclusions deserve our confidence.
I don't think the solution is simply to collect more data or build more powerful AI models. Those developments may help, but they don't eliminate the need to understand the origins, relationships, and limitations of the information being used.
If anything, they make that understanding more important.
When I consider the future of artificial intelligence, I'm increasingly interested in systems that can preserve not only what we know, but also why we believe it, how we came to understand it, and the circumstances under which that understanding might need to change.
That strikes me as a more meaningful ambition than simply making every piece of information searchable or every question answerable.
We've become remarkably good at keeping information alive. The next challenge may be learning how to preserve enough of its meaning that future generations, and the intelligent systems assisting them, can recognize when it deserves to be trusted.
Because the fact that information survived doesn't necessarily mean the truth survived with it.
Continue reading: When Permission Becomes Power