Experts and AI struggle to maintain scientific nuance

The curse of knowledge. Economists Colin Camerer, George Loewenstein, and Martin Weber demonstrated that experts consistently fail to predict how novices interpret information (Big Think). Once individuals acquire deep knowledge, they become anchored in their own informed state and cannot mentally set aside what they know to anticipate a layperson's perspective. This cognitive bias leads experts to present complex proposals that hit a wall because they cannot appreciate the gap between their own expertise and the audience's initial state of ignorance.

Semantic fidelity in AI. Amos Grünebaum warns that AI models frequently erode scientific meaning when tasked with improving readability (MD Intelligence). Models often delete qualifiers like 'may' or 'associated with' to make sentences flow better, which transforms exploratory hypotheses into definitive causal claims. Grünebaum mandates a four-step workflow where users explicitly define constraints against changing numerical values or causal strength to prevent this drift.

Contextual framing of facts. Rachel Pascal argues that all health information is produced within a specific context that shapes the final message, regardless of the writer's intent (Make Sense Health). The same trial result can land as reassurance or a warning depending on whether a writer foregrounds success or the gap in performance (Make Sense Health). Pascal suggests that readers evaluate content by identifying what information the writer places first and what they bury in the middle, as these choices reveal the intended purpose of the communication.