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Technology as Truth? On the Illusion of Objectivity in New Scientific Research Methods

In an era where technology penetrates nearly every aspect of life, science increasingly entrusts its tools of discovery to machines and algorithms. Research methods are no longer driven solely by theoretical innovation but by technical progress: eye-tracking glasses , neural networks, AI-based text analysis, brain imaging. These methods suggest that scientific work is becoming more objective, precise—closer to "truth". But is that really the case? The assumption that technology brings us closer to truth rests on a fundamental confusion: we often equate measurability with objectivity . What can be measured appears indisputable. But what if our tools not only extrapolate our perception of reality—but also limit producing knowledge? What if technological progress is leading us, with seemingly increasing precision, deeper into an illusion? Paradigms Shape Our Reality The idea that science progresses linearly toward truth was challenged by Thomas S. Kuhn in his seminal work Th...

When Calculation Replaces Comprehension: The Limits of a Data-Driven Science

We live in an age intoxicated by numbers. Big Data, AI and quantum computing promise to make science faster, sharper and more objective than ever. We count, model, predict, optimise. On paper, it looks like the triumph of reason. And yet, beneath the glow of dashboards and equations, a quieter question lingers: do numbers alone bring us closer to reality — or just to ever more elegant shadows of it? To understand why data-driven science holds such power today, we must look beyond technology—and return to an older, deeper fascination with mathematics itself. From Pythagoras and Thales to Planck and Einstein, the history of science is also the history of our fascination with mathematics — its beauty, its power, and its blind spot s. Mathematics in Science: The Beauty and the Blind Spots Humans are deeply drawn to structure, and few structures feel as comforting and beautiful as mathematics. There's a quiet thrill in following a complex proof to its clean conclusion, in...

Big Data Satire: When Scientists take their SUV for Grocery Shopping

Some things are so big, they can only exist to cover up something else. In the ’90s and early 2000s, it was cars: SUVs that never saw an off-road trail, tanks on four wheels, needed just to roll to the grocery store. Today, it’s data: Big Data. Massive datasets, proudly paraded like a shiny status symbol. Who needs theory, careful thinking, or painstaking research when you have Big Data? The numbers “speak for themselves,” or so the legend goes. How convenient: Science as a self-service buffet where you dump numbers into a machine and out comes Truth. Meanwhile, pesky questions like “Where did these data come from?” or “What do they actually not tell us?” can be politely ignored. When Size Is Everything Big Data is the academic equivalent of the 90ies “Look at my enormous car!” But Big Data is not the Rolls-Royce, it's the SUV among research practices: The more terabytes, the more prestige. Small datasets are like compact cars—cute but embarrassingly outdated. And anyone still ...

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