Interpretive Blindness
This addresses a problem in epistemology and AI for researchers studying knowledge acquisition from text, but it is incremental as it builds on existing Bayesian frameworks.
The paper models interpretive blindness, an epistemic bias that hinders learning from testimony, showing it arises from co-dependence between beliefs and interpretation in Bayesian settings and the nature of contemporary testimony, particularly argumentative completeness, which can prevent learning even with constraints for good epistemic practices.
We model here an epistemic bias we call \textit{interpretive blindness} (IB). IB is a special problem for learning from testimony, in which one acquires information only from text or conversation. We show that IB follows from a co-dependence between background beliefs and interpretation in a Bayesian setting and the nature of contemporary testimony. We argue that a particular characteristic contemporary testimony, \textit{argumentative completeness}, can preclude learning in hierarchical Bayesian settings, even in the presence of constraints that are designed to promote good epistemic practices.