I think one example of vague language undermining clarity can be found in Joseph Carlsmith's report on AI scheming, which repeatedly uses the term "schemer" to refer to a type of AI that deceives others to seek power. While the report is both extensive and nuanced, and I am definitely not saying the whole report is bad, the document appears to lack a clear, explicit definition of what exactly constitutes a "schemer". For example, using only the language in his report, I cannot determine whether he would consider most human beings schemers, if we consider within-lifetime learning to constitute training. (Humans sometimes lie or deceive others to get control over resources, in ways both big and small. What fraction of them are schemers?)

This lack of definition might not necessarily be an issue in some contexts, as certain words can function informally without requiring precise boundaries. However, in this specific report, the precise delineation of "schemer" is central to several key arguments. He presents specific claims regarding propositions related to AI schemers, such as the likelihood that stochastic gradient descent will find a schemer during training. Without a clear, concrete definition of the term "schemer," it is unclear to me what exactly these arguments are referring to, or what these credences are meant to represent.