The Fragile Balance Between Knowledge, Truth, and Free Speech

A Personal Reflection: The Complexity of Truth

Lately, I had a meeting with one of my favorite clients. I am rarely ever unsure about an outcome because all results are based on data or scientific research. After all, I am not a marketer who makes things up, but a strategist. However, mid-presentation, the client stopped me in my tracks and pointed out that there were a few things that were plainly false.

Interestingly, the slide he was referring to was based on data and studies—yet, he was not wrong. The issue was that it presented only a partial truth. The results were still accurate, but the reasoning behind them was either faulty or too simplistic. This interaction, combined with the recent debate around free speech versus fact-checking, sent me on a quest to reevaluate the nature of knowledge, truth, and the fundamental question: what happens to AI when common-sense knowledge is overridden by the opinions of the loudest voices?

What is Knowledge? A Scientific Exploration

Knowledge has been a subject of philosophical and scientific inquiry for centuries. Across disciplines—from epistemology and neuroscience to artificial intelligence and quantum mechanics—scholars seek to define, measure, and understand knowledge. Examining knowledge from a multidisciplinary perspective allows for a deeper understanding of how it is formed, stored, and utilized by both humans and machines.

The Epistemological Foundations of Knowledge

Epistemology, the branch of philosophy concerned with knowledge, traditionally defines it as "justified true belief." This tripartite model suggests that knowledge consists of three components: belief in a proposition, the truth of that proposition, and justification for the belief. However, philosophical challenges, such as Gettier problems, illustrate cases where justified true beliefs do not necessarily constitute knowledge, leading to refinements in epistemological theories.

Knowledge in Cognitive Science and Neuroscience

From a cognitive and neurological perspective, knowledge is encoded in the human brain through networks of neurons and synaptic connections. Memory structures—such as declarative (explicit) and procedural (implicit) memory—play critical roles in knowledge formation and retrieval.

Cognitive science distinguishes between different types of knowledge:

• Explicit knowledge:?Information that can be consciously recalled and articulated, such as historical facts.
• Tacit knowledge:?Knowledge acquired through experience and practice, often difficult to verbalize (e.g., riding a bicycle).
• Metacognition:?Awareness of one’s own thought processes, influencing how individuals learn and apply knowledge.

Knowledge in Information Theory and Artificial Intelligence

Information theory, pioneered by Claude Shannon, provides a mathematical framework for understanding knowledge as the reduction of uncertainty. In computational sciences, knowledge is often represented as data processed through algorithms, enabling artificial intelligence (AI) systems to simulate human-like reasoning.

AI research categorizes knowledge into:

• Symbolic knowledge representation:?Utilizing logical rules and ontologies.
• Machine learning-based knowledge:?Derived from statistical patterns and neural networks.
• Commonsense knowledge:?Fundamental world knowledge that AI systems struggle to encode effectively.

The intersection of AI and neuroscience has led to advancements in neural networks, mimicking the brain’s ability to acquire, store, and apply knowledge dynamically.

The Question of Ultimate Truth

One of the deepest questions in epistemology and science is whether an ultimate, objective truth exists. While scientific inquiry aims to discover universal principles governing reality, the nature of knowledge is shaped by human perception, cognitive limitations, and contextual interpretation.

Some argue that absolute truth exists but remains inaccessible, while others posit that knowledge is an evolving construct, refined through continuous inquiry and debate. Theories in quantum mechanics, philosophy, and artificial intelligence suggest that truth may be contingent upon the observer, the framework of understanding, and the available evidence.

The Balance Between Fact-Checking and Free Speech

The balance between fact-checking and free speech presents a complex dilemma. On one hand, fact-checking is crucial in preventing the spread of misinformation that can harm society. On the other hand, excessive or biased fact-checking can be perceived as censorship, restricting freedom of speech.

A recent example is Meta’s (formerly Facebook) decision to end its traditional fact-checking program in favor of a Community Notes approach. This move was justified as an effort to promote free speech but has raised concerns about a potential increase in misinformation and hate speech.

Research suggests that certain forms of fact-checking and media literacy can be effective both directly and indirectly. Countries with diverse and independent news media appear to be more resilient against disinformation. However, implementing measures against disinformation without disproportionately restricting free speech remains a challenge.

So where are we going and where will we end up?

There is no easy answer to this dilemma—only time will tell the long-term consequences.

However, if we step back a bit further, we see that the issue about the future of AI also lies in human knowledge itself.? In an old TED Talk, Wade Davis explains:

The world is not one absolute reality but a tapestry of diverse ways of being, thinking, and understanding life. Indigenous cultures, from Amazonian shamans to Tibetan monks, hold unique wisdom that shapes their connection to the Earth. Yet, this vast ethnosphere—the collective dreams, languages, and traditions of humanity—is vanishing at an alarming rate.

Half of the world’s 6,000 languages are no longer spoken by children. Every two weeks, another language disappears forever, taking with it a unique way of seeing the world. Why does this matter? Culture is as vital to our planet as biodiversity. When we lose a language or tradition, we lose a piece of human imagination and resilience.

So if we are already using human knowledge at rapid speed, what does this mean next to a faulty system that is already not necessarily taking into account most knowledge of minorities and lost culture/languages?

If the metaphor of free speech is now used to stop fact-checking, knowledge itself becomes obscured.? AI systems, which rely on data as input, will be shaped by unverified information, potentially embedding falsehoods into automated decision-making processes. This is not just about errors; it is about shaping an AI that learns from misinformation and normalizes bias.

If free speech is used as a shield against accountability, we risk creating a future where knowledge is no longer defined by facts weighing to come close to truth but by the opinions of the loudest voices. The consequence is a world where truth becomes even more fragmented, misinformation flourishes, and AI—designed to augment human intelligence—begins to undermine it.

In this sense, the fight for responsible fact-checking is not just about protecting information integrity—it is about preserving human knowledge itself. Care to dicuss? - Let me know your thoughts.