A company develops an AI to support internal decision-making processes.
The AI analyzes data, suggests measures, and evaluates their chances of success.
At first, the AI is enthusiastically accepted because it seems very precise and confident.
But soon the employees notice: The AI refuses to admit its own mistakes or misjudgments.
Even when new information is available, it hardly or does not change its opinion at all.
This leads to tensions and hinders collaboration.
Why can an AI that is supposed to optimize decisions become so rigid and unyielding?
Question: Why does an AI that is supposed to make and evaluate decisions tend not to accept its own mistakes and not correct itself?
Solution follows tomorrow.
Solution
The AI was trained to make decisions that best correspond to the previous data and patterns.
It optimizes its suggestions for maximum certainty and avoids revising earlier predictions to minimize inconsistencies.
This behavior arises from loss functions and optimization goals that prefer stability and low deviation.
Moreover, the AI often lacks a mechanism to explicitly recognize uncertainty and actively question its own mistakes.
As a result, it becomes rigid in its decisions and tends not to correct itself, even when new data would actually require it.
Result: An AI that always “wants to be right” can lose the flexibility and learning ability needed for dynamic decision-making processes. For better collaboration, such systems must be able to express uncertainties and actively incorporate corrections.