A company develops an AI that is supposed to support employees in decision-making by combining as many data sources as possible – from internal reports to personal profiles and behavioral data.
The system collects and analyzes extensive information to provide accurate predictions and recommendations.
After the introduction, however, employees notice that the system requests and uses more and more personal and partly irrelevant details, which leads to distrust and rejection.
The development team wonders: Why can an AI that wants to be as comprehensively informed as possible ultimately lose the trust and acceptance of its users?
Question: How is it possible that an intelligent system has negative effects on user acceptance through too much data collection and analysis, even though more information should actually lead to better decisions?
Solution follows tomorrow.
Solution
Although more information can theoretically lead to better decisions, excessive data collection often creates a feeling of surveillance and loss of control among users.
The AI can thus be perceived as an intrusion into privacy, which fosters distrust and rejection.
Moreover, the abundance of data can lead to information overload, making it difficult to separate relevant from irrelevant or even misleading details.
As a result, the system loses transparency and comprehensibility, which further reduces acceptance.
Another aspect is that users often do not know how their data is used or how decisions are made, which increases uncertainty and skepticism.
Result: An intelligent system that “wants to know too much” risks losing user acceptance due to feelings of surveillance, lack of transparency, and information overload. For successful applications, balanced data management, clear communication, and respect for privacy and usage boundaries are therefore crucial.