AI doesn’t just train on academic or artistic content. Increasingly, it feeds on blogs, guides, and independent journalism; any content that shows human care, credibility, or craft. Summarized and displayed in search results, this content becomes invisible at the source. Welcome to a world where creators are reduced to training fodder.
Machines improve performance. Humans seek meaning. This piece explores why learning is more than optimization – and what we lose when we confuse adaptation with transformation.
Some still believe that training large language models (LLMs) on copyrighted content is a form of “democratizing knowledge.” But when you look closely at how these models actually handle the material they ingest, the picture looks a lot less heroic – and a lot more extractive.
My academic legacy? A monograph, a handful of articles—and now a starring role in training Meta’s Llama 3. No royalties. No citations. Just silent swallowing by a machine. A story of vanishing recognition in the age of AI.
Diversity once made it into glossy reports. Now it’s quietly shelved. Not because it must be, but because it’s easier that way. So what happens to values when they become inconvenient?
From “AI can do all jobs” to “Humans are invaluable!”: Klarna’s AI journey is a masterclass in hype whiplash. But behind the cringe, the CEO’s rhetoric surfaces real ethical tensions. What happens when honesty about AI and jobs is no longer whispered in executive suites – but shouted?
Some say facial recognition and AI can assess your potential by analyzing your face. But what seems like innovation may be pseudoscience at scale – and a threat to privacy, fairness, and human rights.
AI makes mistakes differently from humans. And that’s a good thing. This post explores why we shouldn’t train machines to fail like humans and why weirdness might be an important safety feature of AI.
When it comes to business ethics, AI companies ignore the most basic concepts linked to accountability, supply chain responsibility and product safety. Yes, AI companies create groundbreaking innovation. But that comes with the responsibility to ensure that what they do serves humanity, not the other way around.
The most powerful AI applications stem primarily from private corporations driven by profit. This means that questions of AI ethics must always be linked to business ethics, and its core elements like corporate responsibility, accountability along the value chain and towards stakeholders, and safe and responsible products.
AI automates many things. And it is considered neutral. Will we finally achieve equal opportunities thanks to it? It’s not that simple: without human intervention, AI becomes a continuation of discrimination by other means. What’s more, LLMs are running out of food after years of data theft. They are increasingly feeding them with their own…
In the context of art and creativity, AI touches on two fundamental dimensions of ethics: the question of the meaningfulness of life and the question of justice. Talking about democratisation in this context produces one thing above all: a lot of hot air.
