Unjustified Confidence and the Violation of Human Subjectivity in ML-Based AI
Scope and Target of This Critique This essay is deliberately limited in scope. The critique applies to machine-learning–based AI systems, particularly large-scale generative models trained via statistical pattern extraction from historical data. It does not address symbolic systems, rule-based automation, or explicitly constrained decision engines. The focus here is not performance, intelligence, or usefulness, but a specific failure mode that emerges from the dominant ML paradigm. Background: Why ML-Based AI Behaves This Way Most contemporary AI systems are built on machine learning architectures that: ...