Epistemic Misalignment in Human-AI Systems: A Four-Quadrant Taxonomy of Uncertainty

Despite significant progress, the AI alignment literature has largely overlooked a philosophical dilemma: \textit{humans and machines represent and communicate uncertainty in fundamentally incompatible ways.} To formalize the arguments around this dilemma, we introduce a four-quadrant taxonomy of uncertainty that partitions uncertainty according to two dimensions: whether uncertainty arises from a human or machine agent, and whether it remains internal or is communicated externally. The taxonomy comprises \textit{human inherent uncertainty} (Bayesian credence), \textit{human self-reported uncertainty} (linguistic hedges), \textit{model inherent uncertainty} (frequentist risk), and \textit{model self-reported uncertainty} (preference-optimized language). We show that this taxonomy reveals an epistemological gap: humans assign uncertainty through grounded, causal understanding, while models assign it through risk-minimization over training data. When models trained with RLHF generate hedged language, humans interpret it as Bayesian belief vis-à-vis frequentist risk-penalty. We close with a practical case study and argue that bridging this epistemic gap is essential - but unfortunately neglected in current discourse - for establishing trust in complex human-AI collaboration modes.