SocietyAI glossary
Bias
Bias inherited from training data: if the internet leans, the model leans. A major issue for automated decisions (hiring, credit…).
In other words
A student who had only revised from a single newspaper: their worldview leans to one side.
Alyriaz articles that mention it
- They ask ChatGPT who to vote for
American voters are preparing their vote with ChatGPT. Six months out from the French presidential election, what that teaches us, and the habits to adopt.
- Doctors put the brakes on medical AI beyond diagnosis
Clinicians are challenging the expansion of medical AI beyond diagnosis, citing weak performance data. We explain why this boundary is not arbitrary.
- AI in recruitment: generated applications, CVs sorted by machine
AI has transformed recruitment on both sides: candidates submit more applications, companies filter more automatically. We analyse this escalation and what it produces.
- Can an AI be neutral?
AI systems are regularly criticised for being politically or culturally biased. But is perfect neutrality even possible for a system trained on human texts and shaped by human choices? We examine where biases originate, and why transparency may be better than the promised neutrality.
- How does an AI "learn"? A model's journey, from raw chaos to the polished assistant
How does a model go from a pile of raw data to an assistant capable of answering you politely? We describe the three main stages of AI training (pre-training, fine-tuning, RLHF) with a simple analogy, to understand where its strengths and biases come from.
- Why the same work is brilliant if it's human and rubbish if it's AI
A simple scientific experiment. You show the same image twice to 565 people. Once saying "made by a human". Once saying "made by an AI". Results: -62% in monetary value, -77% in estimated production time, less perceived creativity, less felt beauty. The same image. That is the exact definition of the word bias.